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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted alphabetically
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 53)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 7)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
Biometrical Journal     Hybrid Journal   (Followers: 6)
Biometrics     Hybrid Journal   (Followers: 51)
Building Simulation     Hybrid Journal   (Followers: 2)
CHANCE     Hybrid Journal   (Followers: 5)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 10)
Computational Statistics     Hybrid Journal   (Followers: 17)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 39)
Current Research in Biostatistics     Open Access   (Followers: 9)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 15)
Demographic Research     Open Access   (Followers: 15)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Extremes     Hybrid Journal   (Followers: 2)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 9)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 3)
Handbook of Statistics     Full-text available via subscription   (Followers: 9)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
International Statistical Review     Hybrid Journal   (Followers: 12)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Applied Statistics     Hybrid Journal   (Followers: 22)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 42, SJR: 3.664, CiteScore: 2)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Econometrics     Hybrid Journal   (Followers: 85)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 8)
Journal of Forecasting     Hybrid Journal   (Followers: 21)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 7)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 35)
Journal of Statistical Physics     Hybrid Journal   (Followers: 12)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 8)
Journal of Statistical Software     Open Access   (Followers: 19, SJR: 13.802, CiteScore: 16)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 78, SJR: 3.746, CiteScore: 2)
Journal of the Korean Statistical Society     Hybrid Journal   (Followers: 1)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 37)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 31)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 43)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 18)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 28)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Lifetime Data Analysis     Hybrid Journal   (Followers: 5)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Metrika     Hybrid Journal   (Followers: 4)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 4)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 9)
Optimization Letters     Hybrid Journal   (Followers: 2)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 35)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 10)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 8)
Review of Socionetwork Strategies     Hybrid Journal  
Risk Management     Hybrid Journal   (Followers: 16)
Sankhya A     Hybrid Journal   (Followers: 3)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Sequential Analysis: Design Methods and Applications     Hybrid Journal   (Followers: 1)
Significance     Hybrid Journal   (Followers: 6)
Sociological Methods & Research     Hybrid Journal   (Followers: 49)
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Stata Journal     Full-text available via subscription   (Followers: 10)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Statistical Methods and Applications     Hybrid Journal   (Followers: 5)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 25)
Statistical Modelling     Hybrid Journal   (Followers: 19)
Statistical Papers     Hybrid Journal   (Followers: 4)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Statistics and Economics     Open Access  
Statistics in Medicine     Hybrid Journal   (Followers: 149)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 12)
Stochastic Models     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
TEST     Hybrid Journal   (Followers: 3)
The American Statistician     Full-text available via subscription   (Followers: 27)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)

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Similar Journals
Journal Cover
Statistical Methods and Applications
Journal Prestige (SJR): 0.466
Citation Impact (citeScore): 1
Number of Followers: 5  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1613-981X - ISSN (Online) 1618-2510
Published by Springer-Verlag Homepage  [2468 journals]
  • Forecasting multidimensional autoregressive time series model with
           symmetric $$\alpha$$ -stable noise using artificial neural networks

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      Abstract: Abstract Artificial neural networks have been widely studied and applied in time series forecasting. However, the existing studies focus more on the univariate Gaussian data. Here, we extend neural network application to multivariate non-Gaussian data, particularly in time series analysis. In this article, we propose a hybrid methodology that combines symmetric \(\alpha\) -stable vector autoregressive time series model with artifical neural networks. The methodology is validated through Monte-Carlo simulations. Moreover, the new method is used to model real empirical data thus showing the usefulness of heavy-tailed models supported by artificial neural networks in statistical modelling.
      PubDate: 2024-07-04
       
  • The local distribution of in-work poverty and sectoral employment: an
           analysis of local dynamics in Italy

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      Abstract: Abstract In-work poverty has risen to become a key feature of European societies. In 2017, the percentage of workers at risk of low pay in Italy reached an estimated 25% and the issue rose to the forefront of the public and political debate. Yet, due to data limitations, few studies analysed the local distribution of this phenomenon and investigated the macro-determinants associated with its rise. By applying Small Area Estimates (SAE) to EU-SILC data we obtain a novel map of the distribution of in-work poverty in Italy, defined as the share of workers at risk of low pay (AROLP) between 2008 and 2017. The unit of analysis of Local Labour Systems, a non-administrative unit based on commuter flows, highlights the deepening of Italian dualism between Northern and Southern areas, as well as rising within-region wage inequality. By means of a panel fixed-effects model linking estimates of AROLP with data on local sectoral employment, we observe that growth in low-skill sectors such as agriculture is associated with increases in AROLP incidence. On the contrary trends of low pay are negatively associated with the growth of manufacturing and construction sectors, and jobs in non-market services, such as public sector jobs. In addition, variations in overall employment represent the strongest predictor for dynamics of low-pay incidence.
      PubDate: 2024-06-17
       
  • Generalized mixed spatiotemporal modeling with a continuous response and
           random effect via factor analysis

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      Abstract: Abstract This work focuses on Generalized Linear Mixed Models that incorporate spatiotemporal random effects structured via Factor Model (FM) with nonlinear interaction between latent factors. A central aspect is to model continuous responses from Normal, Gamma, and Beta distributions. Discrete cases (Bernoulli and Poisson) have been previously explored in the literature. Spatial dependence is established through Conditional Autoregressive (CAR) modeling for the columns of the loadings matrix. Temporal dependence is defined through an Autoregressive AR(1) process for the rows of the factor scores matrix. By incorporating the nonlinear interaction, we can capture more detailed associations between regions and factors. Regions are grouped based on the impact of the main factors or their interaction. It is important to address identification issues arising in the FM, and this study discusses strategies to handle this obstacle. To evaluate the performance of the models, a comprehensive simulation study, including a Monte Carlo scheme, is conducted. Lastly, a real application is presented using the Beta model to analyze a nationwide high school exam called ENEM, administered between 2015 and 2021 to students in Brazil. ENEM scores are accepted by many Brazilian universities for admission purposes. The real analysis aims to estimate and interpret the behavior of the factors and identify groups of municipalities that share similar associations with them.
      PubDate: 2024-05-13
       
  • Prediction in non-sampled areas under spatial small area models

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      Abstract: Abstract In this article we study the prediction problem in small geographic areas in the situation where the survey data does not cover a substantial percentage of these areas. In such situation, the application of the Spatial Fay–Herriot model may involve a difficult and subtle process of determining neighboring areas. Ambiguity in definition of neighbors can potentially produce a problem of sensitivity of the conclusions to these definitions. In this article, we attempt to remedy this problem by incorporating random effects for higher level administrative divisions into the model. In this setting, only the higher-level random effects are supposed to have spatial correlations. This may potentially reduce the problem of ambiguity in the definition of spatial neighbors, provided that all higher level administrative divisions are represented in the sample. We also show that predicting in non-sampled areas is considerably more straightforward under the proposed model, as opposed to the case where the Spatial Fay–Herriot model is applied. In addition, we propose two new predictors for out-of-sample areas, under the spatial Fay–Herriot model. In order to compare the performance of the aforementioned models, we use the data from the Demographic and Family Health Survey of the year 2021, and the National Census carried out in 2017.
      PubDate: 2024-05-13
       
  • Sae estimation of related labor market indicators for different
           overlapping areas

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      Abstract: Abstract The aim of this study is to provide a comprehensive description of the statistical methodology used to produce estimates for various labor market variables at both the City and FUA levels, along with an analysis of the results obtained. To achieve this goal, small area estimates were computed using a unit-level multivariate model. This model was specifically designed to enable coherent estimation of the variables of interest collected by the Labour Force Survey, exploiting information derived from administrative data and statistical Registers. The use of such administrative data at the unit-level represents a novel approach to estimation based on Italian Labour Force Survey data. The estimator used in this work is based on a multivariate model implemented through the Mind R package, which was developed by Istat. The method presented in this study represents an extended multivariate version of the conventional linear mixed model at the unit level. To ensure consistency across different domains, a single cross-classification model was employed, encompassing all relevant domains of interest. The outcomes of this analysis reveal significant improvements in efficiency compared to direct estimates. This is particularly noteworthy in the estimation of unemployed individuals (both total and by gender), where direct estimates are prone to relatively high sampling errors.
      PubDate: 2024-04-18
      DOI: 10.1007/s10260-024-00753-1
       
  • A robust approach for inference on style analysis coefficients

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      Abstract: Abstract Style analysis is an asset class factor model aiming at obtaining information on the internal allocation of a financial portfolio and at comparing portfolios with similar investment strategies. The aim of the paper is to investigate the use of quantile regression to draw inferences on style coefficients. In particular, we compare an approximation widely used from practitioners, the Lobosco–Di Bartolomeo solution, with robust estimators based on constrained median regression. The inference process exploits a rolling window procedure based on subsampling theory. Different sets of outliers have been simulated in order to show differences in the efficiency, in the coverage error and in the length of the resulting confidence intervals. The proposed solution shows better performance in presence of outliers in Y, in X, or in X and Y, in terms both of empirical coverage and of interval lengths, i.e. whereas the performance of the classical solution deteriorates.
      PubDate: 2024-04-17
      DOI: 10.1007/s10260-024-00752-2
       
  • Reverse engineering the last-minute on-line pricing practices: an
           application to hotels

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      Abstract: Abstract We suggest a nonlinear time series methodology to model the (last-minute) price adjustments that hotels active in the online market make to adapt their early-booking rates in response to unpredictable fluctuations in demand. We use this approach to reverse-engineer the pricing strategies of six hotels in Milan, Italy, each with different features and services. The results reveal that the hotels’ ability to align last-minute adjustments with early-booking decisions and account for stochastic demand seasonality varies depending on factors such as size, star rating, and brand affiliation. As a primary empirical finding, we show that the autocorrelations of the first four moments of the last-minute price adjustment can be used to gain crucial insights into the hoteliers’ pricing strategies. Scaling up this approach has the potential to equip policymakers in smart destinations with a reliable and transparent tool for the real-time monitoring of demand dynamics.
      PubDate: 2024-04-04
      DOI: 10.1007/s10260-024-00751-3
       
  • Endogeneity in stochastic frontier models with 'wrong' skewness: copula
           approach without external instruments

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      Abstract: Abstract Stochastic frontier models commonly assume positive skewness for the inefficiency term. However, when this assumption is violated, efficiency scores converge to unity. The potential endogeneity of model regressors introduces another empirical challenge, impeding the identification of causal relationships. This paper tackles these issues by employing an instrument-free estimation method that extends joint estimation through copulas to handle endogenous regressors and skewness issues. The method relies on the Gaussian copula function to capture dependence between endogenous regressors and composite errors with a simultaneous consideration of positively or negatively skewed inefficiency. Model parameters are estimated through maximum likelihood, and Monte Carlo simulations are employed to evaluate the performance of the proposed estimation procedures in finite samples. This research contributes to the stochastic frontier models and production economics literature by presenting a flexible and parsimonious method capable of addressing wrong skewness of inefficiency and endogenous regressors simultaneously. The applicability of the method is demonstrated through an empirical example.
      PubDate: 2024-04-03
      DOI: 10.1007/s10260-024-00750-4
       
  • Multinomial Thompson sampling for rating scales and prior considerations
           for calibrating uncertainty

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      Abstract: Abstract Bandit algorithms such as Thompson sampling (TS) have been put forth for decades as useful tools for conducting adaptively-randomised experiments. By skewing the allocation toward superior arms, they can substantially improve particular outcomes of interest for both participants and investigators. For example, they may use participants’ ratings for continuously optimising their experience with a program. However, most of the bandit and TS variants are based on either binary or continuous outcome models, leading to suboptimal performances in rating scale data. Guided by behavioural experiments we conducted online, we address this problem by introducing Multinomial-TS for rating scales. After assessing its improved empirical performance in unique optimal arm scenarios, we explore potential considerations (including prior’s role) for calibrating uncertainty and balancing arm allocation in scenarios with no unique optimal arms.
      PubDate: 2024-04-01
       
  • Parameter estimation for Logistic errors-in-variables regression under
           case–control studies

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      Abstract: Abstract The article develops parameter estimation in the Logistic regression when the covariate is observed with measurement error. In Logistic regression under the case–control framework, the logarithmic ratio of the covariate densities between the case and control groups is a linear function of the regression parameters. Hence, an integrated least-square-type estimator of the Logistic regression can be obtained based on the estimated covariate densities. When the covariate is precisely measured, the covariate densities can be effectively estimated by the kernel density estimation and the corresponding parameter estimator was developed by Geng and Sakhanenko (2016). When the covariate is observed with measurement error, we propose the least-square-type parameter estimators by adapting the deconvolution kernel density estimation approach. The consistency and asymptotic normality are established when the measurement error in covariate is ordinary smooth. Simulation study shows robust estimation performance of the proposed estimator in terms of bias reduction against the error variance and unbalanced case–control samples. A real data application is also included.
      PubDate: 2024-04-01
       
  • A new Bayesian discrepancy measure

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      Abstract: Abstract The aim of this article is to make a contribution to the Bayesian procedure of testing precise hypotheses for parametric models. For this purpose, we define the Bayesian Discrepancy Measure that allows one to evaluate the suitability of a given hypothesis with respect to the available information (prior law and data). To summarise this information, the posterior median is employed, allowing a simple assessment of the discrepancy with a fixed hypothesis. The Bayesian Discrepancy Measure assesses the compatibility of a single hypothesis with the observed data, as opposed to the more common comparative approach where a hypothesis is rejected in favour of a competing hypothesis. The proposed measure of evidence has properties of consistency and invariance. After presenting the definition of the measure for a parameter of interest, both in the absence and in the presence of nuisance parameters, we illustrate some examples showing its conceptual and interpretative simplicity. Finally, we compare a test procedure based on the Bayesian Discrepancy Measure, with the Full Bayesian Significance Test, a well-known Bayesian testing procedure for sharp hypotheses.
      PubDate: 2024-04-01
       
  • Online job ads in Italy: a regional analysis of ICT professionals

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      Abstract: Abstract In European countries, there is a growing interest in integrating traditional statistical sources on the labour market with online job ads. They offer detailed and timely information on the use of the Internet for recruiting and the specific skills required at different levels (particularly at a territorial and sectoral level). In this context, this paper proposes an analysis of the similarity between the Italian regions regarding required skills by employers. The study looks at a specific group of innovation-related occupations, ICT professionals, that are believed to be sufficiently represented by online data. The results highlight a regional gap in the use of online offers and differences in professional profiles regarding required skills. Finally, regional skill similarities are compared with some regional features related to the labour market and training.
      PubDate: 2024-04-01
       
  • A copula-based portrayal of the collider bias

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      Abstract: Abstract This article proposes to use copulas to characterize the collider bias that concerns the non-substantive change in the causal dependence between variables before and after conditioning on their common effect (collider). This copula-based portrayal allows scholars (1) to capture the sophisticated (e.g., asymmetric or heavy-tail) causal dependence structure that is usually not evidenced by a summative causal effect estimate, such as the regression coefficient based on a well-matched sample; (2) to focus on the causal dependence structure that is insensitive to the influences from the marginal distributions; and (3) to directly and formally test the significance of change in the causal dependence structure using the Cramér–von Mises statistic. Both simulation and real data examples are presented, which suggest that copulas can be a handy tool for practical researchers to describe the collider bias.
      PubDate: 2024-04-01
       
  • Point and probabilistic forecast reconciliation for general linearly
           constrained multiple time series

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      Abstract: Abstract Forecast reconciliation is the post-forecasting process aimed to revise a set of incoherent base forecasts into coherent forecasts in line with given data structures. Most of the point and probabilistic regression-based forecast reconciliation results ground on the so called “structural representation” and on the related unconstrained generalized least squares reconciliation formula. However, the structural representation naturally applies to genuine hierarchical/grouped time series, where the top- and bottom-level variables are uniquely identified. When a general linearly constrained multiple time series is considered, the forecast reconciliation is naturally expressed according to a projection approach. While it is well known that the classic structural reconciliation formula is equivalent to its projection approach counterpart, so far it is not completely understood if and how a structural-like reconciliation formula may be derived for a general linearly constrained multiple time series. Such an expression would permit to extend reconciliation definitions, theorems and results in a straightforward manner. In this paper, we show that for general linearly constrained multiple time series it is possible to express the reconciliation formula according to a “structural-like” approach that keeps distinct free and constrained, instead of bottom and upper (aggregated), variables, establish the probabilistic forecast reconciliation framework, and apply these findings to obtain fully reconciled point and probabilistic forecasts for the aggregates of the Australian GDP from income and expenditure sides, and for the European Area GDP disaggregated by income, expenditure and output sides and by 19 countries.
      PubDate: 2024-04-01
       
  • Integrating probability and big non-probability samples data to produce
           Official Statistics

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      Abstract: Abstract This paper introduces the pseudo-calibration estimators, a novel method that integrates a non-probability sample of big size with a probability sample, assuming both samples contain relevant information for estimating the population parameter. The proposed estimators share a structural similarity with the adjusted projection estimators and the difference estimators but they adopt a different inferential approach and informative setup. The pseudo-calibration estimators can be employed when the target variable is observed in the probability sample and, in the non-probability sample, it is observed correctly, observed with error, or predicted. This paper also introduces an original application of the jackknife-type method for variance estimation. A simulation study shows that the proposed estimators are robust and efficient compared to the regression data integration estimators that use the same informative setup. Finally, a further evaluation using real data is carried out.
      PubDate: 2024-04-01
       
  • Supervised classification of spatial epidemics incorporating infection
           time uncertainty

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      Abstract: Abstract Mechanistic models are key to providing reliable information for developing infectious disease control strategies. In general, these models are fitted in Bayesian Markov chain Monte Carlo (MCMC) frameworks that incorporate heterogeneities within a population. However, these frameworks have the major drawback of being computationally expensive. This problem is even more severe when the epidemic history is incomplete, such as unknown infection times. Instead of using the time-consuming Bayesian MCMC methods, this paper explores the use of supervised classification methods to analyze the infectious disease data incorporating infection time uncertainty. The epidemic generating models are classified based on summary statistics of epidemics as inputs. The validity of these methods is investigated by using simulated epidemic data and Tomato Spotted Wilt Virus (TSWV) data, accounting for unknown infectious periods and infection times of individuals. We show that these methods are capable of capturing biological characteristics of disease transmission dynamics when there is infection time uncertainty in infectious disease data.
      PubDate: 2024-04-01
       
  • Understanding relationships with the Aggregate Zonal Imbalance using
           copulas

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      Abstract: Abstract In the Italian electricity market, we analyze the Aggregate Zonal Imbalance, which is the algebraic sum, changed in sign, of the amount of energy procured by the Italian national Transmission and System Operator in the Dispatching Services Market at a given time in the northern Italian electricity macro-zone. Specifically, we determine possible relationships among the Aggregate Zonal Imbalances and other variables of interest in electricity markets, including renewable sources. From a methodological point of view, we use a multivariate model for time series that combines the marginal behavior with copula-type models. As a result, the flexibility of a copula approach will allow identifying the nature of non-linear linkages among the Aggregate Zonal Imbalance and other variables such as forecasted demand, forecasted wind and solar PV generation. In this respect, novel ways to measure dependence and association among random variates are adopted. Our results indicate a clear association between the Aggregate Zonal Imbalance and Forecasted Solar PV generation, and a weaker relationship with the other considered variables. We find this result both in terms of pairwise Spearman’s and Kendall’s correlations and in terms of upper and lower tail dependence. The analysis concludes with the proposal of new indicators to detect association among random vectors, which could identify the more important features driving imbalances.
      PubDate: 2024-04-01
       
  • The parsimonious Gaussian mixture models with partitioned parameters and
           their application in clustering

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      Abstract: Abstract Cluster analysis is a method that identifies similar groups of data without any prior knowledge of the relevant groups. One of the most widely used clustering methods is model-based clustering, in which data clustering is performed by fitting a probabilistic model to the data. Mixture of Gaussian distributions is a commonly used model in model-based clustering. Unfortunately, the number of covariance matrices parameters rapidly increases by increasing the number of variables or components in these models. So far, various classes of the parsimonious Gaussian mixture models, by applying various constraints on the covariance matrices, have been introduced to solve this problem. Unfortunately, the number of models in each of these classes is so small such that in practice it does not allow the study and selection of models with any number of parameters, which can vary between the minimum number (one parameter) and the maximum number (no constraints model) of parameters. In this paper, to deal with this problem a family of the parsimonious Gaussian mixture models is introduced. This is done by identifying and determining the appropriate partitions of the variances and correlation coefficients between variables among clusters. We call these models “the parsimonious Gaussian mixture models with partitioned parameters". The generalized Expectation-Conditional Maximization algorithm, by employing the Fisher scoring method within the algorithm, is used to compute the maximum likelihood estimates of parameters. Bayesian information criterion is used for comparing and selecting the best model. Also, the steepest ascent method is adapted to search the best model. Finally, performances of these models are examined on two real datasets and a brief simulation study.
      PubDate: 2024-04-01
       
  • Semiparametric transformation model in presence of cure fraction: a
           hierarchical Bayesian approach assuming the unknown hazards as latent
           factors

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      Abstract: Abstract The class of semiparametric or transformation models has been presented in the literature as a promising alternative for the analysis of lifetime data in the presence of covariates and censored data. This class of models generalizes the popular class of proportional hazards models proposed by Cox (J R Stat Soc: Ser B (Methodol) 34(2):187–202, 1972) where it is not needed to assume a parametric probability distribution for the survival times. In addition to our focus on semipametric models, we also explore the situation where the population of interest is a mixture of susceptible individuals, who experience the event of interest and non-susceptible individuals that will never experience the event of interest. These individuals are not at risk with respect to the event of interest and are considered immune, non-susceptible, or cured. In this study, we present a simple method to obtain inferences for the parameters of semiparametric or transformation models in the presence of censoring, covariates and cure fraction under a Bayesian approach assuming the unknown hazard rates as latent variables with a given probability distribution. The posterior summaries of interest are obtained using existing Markov Chain Monte Carlo (MCMC) simulation methods. Some applications with real medical survival data illustrate the proposed methodology.
      PubDate: 2024-04-01
       
  • A predictive model for planning emergency events rescue during COVID-19 in
           Lombardy, Italy

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      Abstract: Abstract Forecasting the volume of emergency events is important for resource utilization in emergency medical services (EMS). This became more evident during the COVID-19 outbreak when emergency event forecasts used by various EMS at that time tended to be inaccurate due to fluctuations in the number, type, and geographical distribution of these events. The motivation for this study was to develop a statistical model capable of predicting the volume of emergency events for Lombardy’s regional EMS called AREU at different time horizons. To accomplish this goal, we propose a negative binomial additive autoregressive model with smoothing splines, which can predict over-dispersed counts of emergency events one, two, five, and seven days ahead. In the model development stage, a large set of covariates was considered, and the final model was selected using a cross-validation procedure that takes into account the observations’ temporal dependence. Comparisons of the forecasting performance using the mean absolute percentage error showed that the proposed model outperformed the model used by AREU, as well as other widely used forecasting models. Consequently, AREU decided to adopt the new model for its forecasting purposes.
      PubDate: 2024-04-01
       
 
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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted alphabetically
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 53)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 7)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
Biometrical Journal     Hybrid Journal   (Followers: 6)
Biometrics     Hybrid Journal   (Followers: 51)
Building Simulation     Hybrid Journal   (Followers: 2)
CHANCE     Hybrid Journal   (Followers: 5)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 10)
Computational Statistics     Hybrid Journal   (Followers: 17)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 39)
Current Research in Biostatistics     Open Access   (Followers: 9)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 15)
Demographic Research     Open Access   (Followers: 15)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Extremes     Hybrid Journal   (Followers: 2)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 9)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 3)
Handbook of Statistics     Full-text available via subscription   (Followers: 9)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
International Statistical Review     Hybrid Journal   (Followers: 12)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Applied Statistics     Hybrid Journal   (Followers: 22)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 42, SJR: 3.664, CiteScore: 2)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Econometrics     Hybrid Journal   (Followers: 85)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 8)
Journal of Forecasting     Hybrid Journal   (Followers: 21)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 7)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 35)
Journal of Statistical Physics     Hybrid Journal   (Followers: 12)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 8)
Journal of Statistical Software     Open Access   (Followers: 19, SJR: 13.802, CiteScore: 16)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 78, SJR: 3.746, CiteScore: 2)
Journal of the Korean Statistical Society     Hybrid Journal   (Followers: 1)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 37)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 31)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 43)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 18)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 28)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Lifetime Data Analysis     Hybrid Journal   (Followers: 5)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Metrika     Hybrid Journal   (Followers: 4)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 4)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 9)
Optimization Letters     Hybrid Journal   (Followers: 2)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 35)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 10)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 8)
Review of Socionetwork Strategies     Hybrid Journal  
Risk Management     Hybrid Journal   (Followers: 16)
Sankhya A     Hybrid Journal   (Followers: 3)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Sequential Analysis: Design Methods and Applications     Hybrid Journal   (Followers: 1)
Significance     Hybrid Journal   (Followers: 6)
Sociological Methods & Research     Hybrid Journal   (Followers: 49)
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Stata Journal     Full-text available via subscription   (Followers: 10)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Statistical Methods and Applications     Hybrid Journal   (Followers: 5)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 25)
Statistical Modelling     Hybrid Journal   (Followers: 19)
Statistical Papers     Hybrid Journal   (Followers: 4)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Statistics and Economics     Open Access  
Statistics in Medicine     Hybrid Journal   (Followers: 149)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 12)
Stochastic Models     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
TEST     Hybrid Journal   (Followers: 3)
The American Statistician     Full-text available via subscription   (Followers: 27)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)

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