A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

              [Sort alphabetically]   [Restore default list]

  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted by number of followers
Review of Economics and Statistics     Hybrid Journal   (Followers: 154)
Statistics in Medicine     Hybrid Journal   (Followers: 150)
Journal of Econometrics     Hybrid Journal   (Followers: 83)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 72, SJR: 3.746, CiteScore: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 53)
Biometrics     Hybrid Journal   (Followers: 52)
Sociological Methods & Research     Hybrid Journal   (Followers: 45)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 41)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 40, SJR: 3.664, CiteScore: 2)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 37)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 35)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 33)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 33)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 30)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 28)
The American Statistician     Full-text available via subscription   (Followers: 26)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 24)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 24)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Forecasting     Hybrid Journal   (Followers: 20)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 18)
Statistical Modelling     Hybrid Journal   (Followers: 18)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
Journal of Statistical Software     Open Access   (Followers: 16, SJR: 13.802, CiteScore: 16)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 16)
Risk Management     Hybrid Journal   (Followers: 16)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 15)
Computational Statistics     Hybrid Journal   (Followers: 15)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Demographic Research     Open Access   (Followers: 14)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 13)
Journal of Statistical Physics     Hybrid Journal   (Followers: 13)
International Statistical Review     Hybrid Journal   (Followers: 12)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 12)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 11)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 11)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Journal of Probability and Statistics     Open Access   (Followers: 10)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Biometrical Journal     Hybrid Journal   (Followers: 9)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Argumentation et analyse du discours     Open Access   (Followers: 8)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Current Research in Biostatistics     Open Access   (Followers: 8)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Stata Journal     Full-text available via subscription   (Followers: 8)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 8)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 7)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
Lifetime Data Analysis     Hybrid Journal   (Followers: 7)
Significance     Hybrid Journal   (Followers: 7)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 7)
Queueing Systems     Hybrid Journal   (Followers: 7)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Statistical Methods and Applications     Hybrid Journal   (Followers: 6)
Law, Probability and Risk     Hybrid Journal   (Followers: 6)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
Journal of Global Optimization     Hybrid Journal   (Followers: 6)
Applied Categorical Structures     Hybrid Journal   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Engineering With Computers     Hybrid Journal   (Followers: 5)
CHANCE     Hybrid Journal   (Followers: 5)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 4)
Metrika     Hybrid Journal   (Followers: 4)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Statistical Papers     Hybrid Journal   (Followers: 4)
Sankhya A     Hybrid Journal   (Followers: 3)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 3)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
Building Simulation     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Stochastic Models     Hybrid Journal   (Followers: 2)
Optimization Letters     Hybrid Journal   (Followers: 2)
TEST     Hybrid Journal   (Followers: 2)
Extremes     Hybrid Journal   (Followers: 2)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Statistics and Economics     Open Access  
Review of Socionetwork Strategies     Hybrid Journal  
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Journal of the Korean Statistical Society     Hybrid Journal  
Sequential Analysis: Design Methods and Applications     Hybrid Journal  

              [Sort alphabetically]   [Restore default list]

Similar Journals
Journal Cover
Statistical Methods and Applications
Journal Prestige (SJR): 0.466
Citation Impact (citeScore): 1
Number of Followers: 6  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1613-981X - ISSN (Online) 1618-2510
Published by Springer-Verlag Homepage  [2469 journals]
  • Correction to: Detecting economic insecurity in Italy: a latent transition
           modelling approach

    • Free pre-print version: Loading...

      PubDate: 2022-10-01
       
  • Detecting economic insecurity in Italy: a latent transition modelling
           approach

    • Free pre-print version: Loading...

      Abstract: Abstract Economic insecurity has increased in importance in the understanding of economic and socio-demographic household behaviour. The present paper aims to analyse patterns of household economic insecurity over the years 2004–2015 by using the longitudinal section of the Italian SILC (Statistics on Income and Living Conditions) survey. In the identification of economic insecurity statuses, we used indicators of economic hardship in a latent transition approach in order to: (i) classify Italian households into homogenous classes characterised by different levels of economic insecurity, (ii) assess whether changes in latent class membership occurred in the selected time span, and (iii) evaluate the effect of employment status and characteristics of individuals on latent status membership. Empirical findings uncovered five latent statuses of economic insecurity from the best situation to the worst. The levels of economic insecurity remained quite stable over the period considered, but a non-negligible worsening can be detected for the unemployed and individuals with part-time jobs.
      PubDate: 2022-10-01
       
  • Networks as mediating variables: a Bayesian latent space approach

    • Free pre-print version: Loading...

      Abstract: Abstract The use of network analysis to investigate social structures has recently seen a rise due to the high availability of data and the numerous insights it can provide into different fields. Most analyses focus on the topological characteristics of networks and the estimation of relationships between the nodes. We adopt a different perspective by considering the whole network as a random variable conveying the effect of an exposure on a response. This point of view represents a classical mediation setting, where the interest lies in estimating the indirect effect, that is, the effect propagated through the mediating variable. We introduce a latent space model mapping the network into a space of smaller dimension by considering the hidden positions of the units in the network. The coordinates of each node are used as mediators in the relationship between the exposure and the response. We further extend mediation analysis in the latent space framework by using Generalised Linear Models instead of linear ones, as previously done in the literature, adopting an approach based on derivatives to obtain the effects of interest. A Bayesian approach allows us to get the entire distribution of the indirect effect, generally unknown, and compute the corresponding highest density interval, which gives accurate and interpretable bounds for the mediated effect. Finally, an application to social interactions among a group of adolescents and their attitude toward substance use is presented.
      PubDate: 2022-10-01
       
  • Sensitivity analysis for unobserved confounding in causal mediation
           analysis allowing for effect modification, censoring and truncation

    • Free pre-print version: Loading...

      Abstract: Abstract Causal mediation analysis is used to decompose the total effect of an exposure on an outcome into an indirect effect, taking the path through an intermediate variable, and a direct effect. To estimate these effects, strong assumptions are made about unconfoundedness of the relationships between the exposure, mediator and outcome. These assumptions are difficult to verify in a given situation and therefore a mediation analysis should be complemented with a sensitivity analysis to assess the possible impact of violations. In this paper we present a method for sensitivity analysis to not only unobserved mediator-outcome confounding, which has largely been the focus of previous literature, but also unobserved confounding involving the exposure. The setting is estimation of natural direct and indirect effects based on parametric regression models. We present results for combinations of binary and continuous mediators and outcomes and extend the sensitivity analysis for mediator-outcome confounding to cases where the continuous outcome variable is censored or truncated. The proposed methods perform well also in the presence of interactions between the exposure, mediator and observed confounders, allowing for modeling flexibility as well as exploration of effect modification. The performance of the method is illustrated through simulations and an empirical example.
      PubDate: 2022-10-01
       
  • Impact measurement and dimension reduction of auxiliary variables in
           calibration estimator using the Shapley decomposition

    • Free pre-print version: Loading...

      Abstract: Abstract In multipurpose surveys several interest variables and a very large number of auxiliary variables are collected. Auxiliary variables are usually considered in calibration for improving estimates. But, very often, some of them are included for the sole purpose of increasing consistency. Consistency is an important point for National Statistical Institutes especially as a means for promoting credibility in published statistics. As a direct result, the number of auxiliary variables considered in calibration continue to grow over time. In literature, several methods show how to manage many auxiliary variables in order to prevent some unpleasant consequences on the accuracy of estimates. They consist mainly in variable selection or dimension reduction and they are very useful for deriving calibrated estimates more accurately. However, looking at them, it is not easy to infer how much the contribution of each auxiliary variable is, especially when there are plenty of them. The Shapley decomposition applied in the calibration context could be a useful tool to better understand the net effects of auxiliary variables, and, in addition, it provides further information for supporting researchers in choosing the best calibration system. It provides a direct measure of the change with respect to Horvitz–Thompson estimates and to related sampling variances due to the introduction of each auxiliary variable in the calibration. The method has been applied to real data of the Italian Labour Force Survey that makes an extensive use of auxiliary variables in calibration.
      PubDate: 2022-10-01
       
  • Effective transfer entropy to measure information flows in credit markets

    • Free pre-print version: Loading...

      Abstract: Abstract In this paper we propose to study the dynamics of financial contagion between the credit default swap (CDS) and the sovereign bond markets through effective transfer entropy, a model-free methodology which enables to overcome the required hypotheses of classical price discovery measures in the statistical and econometric literature, without being restricted to linear dynamics. By means of effective transfer entropy we correct for small sample biases which affect the traditional Shannon transfer entropy, as well as we are able to conduct inference on the estimated directional information flows. In our empirical application, we analyze the CDS and bond market data for eight countries of the European Union, and aim to discover which of the two assets is faster at incorporating the information on the credit risk of the underlying sovereign. Our results show a clear and statistically significant prominence of the bond market for pricing the sovereign credit risk, especially during the crisis period. During the post-crisis period, instead, a few countries behave dissimilarly from the others, in particular Spain and the Netherlands.
      PubDate: 2022-10-01
       
  • Inference for non-probability samples under high-dimensional
           covariate-adjusted superpopulation model

    • Free pre-print version: Loading...

      Abstract: Abstract Non-probability samples become increasingly popular in sampling survey with lower costs, shorter time durations and higher efficiencies. In the high-dimensional superpopulation modeling approach for non-probability samples, a model is fitted for the analysis variable from a non-probability sample, and is used to project the sample to the full population. In practice, there exist situations that the covariates in modeling process are not directly observed, but are contaminated with a multiplicative factor that is determined by the value of an unknown function of an observable confounder. In the paper, we propose to calibrate the covariates by nonparametrically regressing the observable contaminated covariate on the confounder. We employ the SCAD-penalized least squares method to investigate the variable selection and inference problems for non-probability samples based on the calibrated covariates. A SCAD-penalized estimator for the parameter and the population mean estimator are obtained. Under some mild assumptions, we establish the “oracle property” of the proposed SCAD-penalized estimator and give the consistency properties of the proposed population mean estimator. Simulation studies are conducted to assess the finite-sample performance of the proposed method. An application to a Boston housing price study demonstrates the utility of the proposed method in practice.
      PubDate: 2022-10-01
       
  • Control charts for monitoring the median in non-negative asymmetric data

    • Free pre-print version: Loading...

      Abstract: Abstract Control charts are commonly used for monitoring the mean of processes. However, there are practical applications in which asymmetric data are the standard. In these scenarios, the use of robust statistics, such as the median, is advantageous over the mean. Based on this, we propose an empirical control chart for monitoring the median of a wide class of distributions, known as the log-symmetric class. Closed-form estimators, which perform better than the maximum likelihood estimator, are considered. Simulation studies are carried out with the following objectives: to evaluate the in-control and the out-control average run length; to evaluate the behavior of the control limits; and to compare the proposed method with a naive method based on the asymptotic distribution of the three estimators. The results indicate that the proposed approach presents better in-control average run length than the naive method and better power of detection for negative shifts in the median. A practical use of the proposed approach is illustrated with a real engineering problem, followed by a goodness of fit based on AIC and BIC, considering the most common asymmetric distributions. We also perform a residual analysis with the chosen distribution to verify its fit. Finally, based on the chosen distribution the proposed method indicates that there is an out-of-control point in phase II, which is not detected by the naive approach. Therefore, showing a gain in using the proposed method.
      PubDate: 2022-10-01
       
  • A COVINDEX based on a GAM beta regression model with an application to the
           COVID-19 pandemic in Italy

    • Free pre-print version: Loading...

      Abstract: Abstract Detecting changes in COVID-19 disease transmission over time is a key indicator of epidemic growth. Near real-time monitoring of the pandemic growth is crucial for policy makers and public health officials who need to make informed decisions about whether to enforce lockdowns or allow certain activities. The effective reproduction number \(R_t\) is the standard index used in many countries for this goal. However, it is known that due to the delays between infection and case registration, its use for decision making is somewhat limited. In this paper a near real-time COVINDEX is proposed for monitoring the evolution of the pandemic. The index is computed from predictions obtained from a GAM beta regression for modelling the test positive rate as a function of time. The proposal is illustrated using data on COVID-19 pandemic in Italy and compared with \(R_t\) . A simple chart is also proposed for monitoring local and national outbreaks by policy makers and public health officials.
      PubDate: 2022-10-01
       
  • Model-assisted calibration with SCAD to estimated control for
           non-probability samples

    • Free pre-print version: Loading...

      Abstract: Abstract Non-probability samples have been used in various fields in recent years. However, they usually can result in biased estimates. Calibration to estimated control has been proposed to reduce bias from non-probability samples. The relationship models between the study variable and covariates will help to improve the efficiency of calibration. Specifically, the selection of important covariates is a key issue in establishing the relationship models. In this paper, model-assisted calibration to estimated control using the smoothly clipped absolute deviation (SCAD) is proposed to make inference from non-probability samples. Instead of the traditional chi-square distance, the modified forward Kullback–Leibler distance is explored in the proposed method and the corresponding asymptotic properties are derived. Moreover, the classical variable selection approach SCAD is also implemented to conduct both variable selection and parameter estimation in establishing the relationship models for calibration. The performances of the proposed method are investigated through simulation studies, and an application to analyze a non-probability sample from the National Health Interview Survey in 2017.
      PubDate: 2022-10-01
       
  • Distortion representations of multivariate distributions

    • Free pre-print version: Loading...

      Abstract: Abstract The univariate distorted distributions were introduced in risk theory to represent changes (distortions) in the expected distributions of some risks. Later, they were also applied to represent distributions of order statistics, coherent systems, proportional hazard rate and proportional reversed hazard rate models, etc. In this paper we extend this concept to the multivariate setup. We show that, in some cases, they are a valid alternative to the copula representation, especially when the marginal distributions may not be easily handled. Several examples illustrate the applications of such representations in statistical modeling. They include the study of paired (dependent) ordered data, joint residual lifetimes, order statistics and coherent systems
      PubDate: 2022-10-01
       
  • Bayesian network structural learning from complex survey data: a
           resampling based approach

    • Free pre-print version: Loading...

      Abstract: Abstract Nowadays there is increasing availability of good quality official statistics data. The construction of multivariate statistical models possibly leading to the identification of causal relationships is of interest. In this context Bayesian networks play an important role. A crucial step consists in learning the structure of a Bayesian network. One of the most widely used procedures is the PC algorithm consisting in carrying out several independence tests on the available data set and in building a Bayesian network according to the tests results. The PC algorithm is based on the irremissible assumption that data are independent and identically distributed. Unfortunately, official statistics data are generally collected through complex sampling designs, then the aforementioned assumption is not met. In such a context the PC algorithm fails in learning the structure. To avoid this, the sample selection must be taken into account in the structural learning process. In this paper, a modified version of the PC algorithm is proposed for inferring causal structure from complex survey data. It is based on resampling techniques for finite populations. A simulation experiment showing the robustness with respect to departures from the assumptions and the good performance of the proposed algorithm is carried out.
      PubDate: 2022-10-01
       
  • Bridge closure in the road network of Lombardy: a spatio-temporal analysis
           of the socio-economic impacts

    • Free pre-print version: Loading...

      Abstract: Abstract This paper introduces a methodology to evaluate the socio-economic impacts of closure for maintenance of one or more infrastructures of a large and complex road network. Motivated by a collaboration with Regione Lombardia, we focus on a subset of bridges in the region, although we aim at developing a method scalable to all road infrastructures of the regional network, consisting of more than 10,000 tunnels, bridges and overpasses. The final aim of the endeavor is to help decision-makers in prioritizing their interventions for maintaining and repairing infrastructure segments. We develop two different levels of impact assessment, both providing a unique global score for each bridge closure and investigating its spatio-temporal effects on mobility. We take advantage of a functional data analysis approach enhanced by a complex network theory perspective, thus modelling the roads of Lombardy as a network in which weights attributed to the edges are functional data. Results reveal the most critical bridges of Lombardy; moreover, for each bridge closure, the most impactful hours of the day and the most impacted municipalities of the region are identified. The proposed approach develops a flexible and scalable method for monitoring infrastructures of large and complex road networks.
      PubDate: 2022-10-01
       
  • Bayesian GARCH modeling of functional sports data

    • Free pre-print version: Loading...

      Abstract: Abstract The use of statistical methods in sport analytics has gained a rapidly growing interest over the last decade, and nowadays is common practice. In particular, the interest in understanding and predicting an athlete’s performance throughout his/her career is motivated by the need to evaluate the efficacy of training programs, anticipate fatigue to prevent injuries and detect unexpected of disproportionate increases in performance that might be indicative of doping. Moreover, fast evolving data gathering technologies require up to date modelling techniques that adapt to the distinctive features of sports data. In this work, we propose a hierarchical Bayesian model for describing and predicting the evolution of performance over time for shot put athletes. We rely both on a smooth functional contribution and on a linear mixed effect model with heteroskedastic errors to represent the athlete-specific trajectories. The resulting model provides an accurate description of the performance trajectories and helps specifying both the intra- and inter-seasonal variability of measurements. Further, the model allows for the prediction of athletes’ performance in future sport seasons. We apply our model to an extensive real world data set on performance data of professional shot put athletes recorded at elite competitions.
      PubDate: 2022-09-16
       
  • Alternative sensitivity analyses for regression estimates of treatment
           effects to unobserved confounding in binary and survival data

    • Free pre-print version: Loading...

      Abstract: Abstract Estimates of treatment effects in non-experimental studies are subject to bias owing to unobserved confounding. It is desirable to assess the sensitivity of an estimated treatment effect to a hypothetical unmeasured confounder, U. A commonly used approach to sensitivity analysis requires two parameters: one parameter relates U to the treatment and the other relates it to the outcome. The method uses a simple algebraic formula with these two parameters to relate the true treatment effect to the apparent treatment effect, obtained from a reduced model without U. This formula approximately holds for logistic and proportional hazards models, which are frequently used to model binary and survival outcomes. This approximation works with an assumption that the absolute regression coefficient for the unmeasured confounder is small. Therefore, when the unmeasured confounding is relatively large, the formula will not perform well. In this article, we propose alternative sensitivity analysis methods for binary and survival outcomes. We develop sensitivity analysis formulas for treatment effect estimates under probit and additive hazard models, which are alternatives to the logistic and proportional hazards models, respectively. The proposed formulae hold without any approximations. We also discuss a method to postulate reasonable values of the sensitivity parameters using the observed covariates. Simulation studies demonstrate that the proposed formulae perform well for moderate and severe unmeasured confounding even when the model used for the sensitivity analysis is moderately mis-specified. The practical utility of the approach is illustrated in two example studies.
      PubDate: 2022-09-01
       
  • Group penalized quantile regression

    • Free pre-print version: Loading...

      Abstract: Abstract Quantile regression models have become a widely used statistical tool in genetics and in the omics fields because they can provide a rich description of the predictors’ effects on an outcome without imposing stringent parametric assumptions on the outcome-predictors relationship. This work considers the problem of selecting grouped variables in high-dimensional linear quantile regression models. We introduce a group penalized pseudo quantile regression (GPQR) framework with both group-lasso and group non-convex penalties. We approximate the quantile regression check function using a pseudo-quantile check function. Then, using the majorization–minimization principle, we derive a simple and computationally efficient group-wise descent algorithm to solve group penalized quantile regression. We establish the convergence rate property of our algorithm with the group-Lasso penalty and illustrate the GPQR approach performance using simulations in high-dimensional settings. Furthermore, we demonstrate the use of the GPQR method in a gene-based association analysis of data from the Alzheimer’s Disease Neuroimaging Initiative study and in an epigenetic analysis of DNA methylation data.
      PubDate: 2022-09-01
       
  • Generalized proportional reversed hazard rate distributions with
           application in medicine

    • Free pre-print version: Loading...

      Abstract: Abstract Proportional reversed hazard rate distribution family plays an important role in the reliability and lifetime data modelling. In this manuscript this family of distributions is generalized in order to enhance its modelling capability. Considering a special case, we introduce the extended generalized exponential distribution. Its mathematical properties are also studied. New model is applied in fitting levels of TSH hormone and remission times of bladder cancer patients. We believe these results may attract applied statisticians especially those who are in charge with life time data analysis.
      PubDate: 2022-09-01
       
  • Evidences from survey data and fiscal data: nonresponse and measurement
           errors in annual incomes

    • Free pre-print version: Loading...

      Abstract: Abstract A (local) survey on income carried out in the city of Modena in 2002, with income reference year 2001, generated four categories of units: interviewees, refusals, noncontacts, and unused reserves . In this study, all units were matched with their corresponding records in the Ministry of Finance 2001 database and the 2001 Census database. Considering all four categories, participation increased by education level and activity status, while it decreased among low or high incomes. Considering interviewees only, over- and under-reporting, as well as measurement errors, were investigated by comparing the surveyed income with fiscal income. Age and level of income were the main covariates affecting the behaviours of taxpayers.
      PubDate: 2022-09-01
       
  • Queue hurdle Coxian phase-type model for two-stage process of
           population-based cancer screening

    • Free pre-print version: Loading...

      Abstract: Abstract The quality assurance of two-stage population-based cancer screening program is determined by arrival rate (attending screening), positive rate (determined by the criteria of screening test), the compliance and the waiting time (WT) for confirmatory diagnosis in those screened as positive. These parameters were correlated between the process of screening procedures and the effectiveness of screening program. To capture such an inter-dependence of these parameters and quantify the effectiveness of program, we proposed a Queue hurdle Coxian phase-type (QH-CPH) model to estimate the arrival rate of screenees with the Poisson Queue process and the compliance rate of confirmatory diagnosis with the hurdle model, and also to identify the hidden states of WT that is affected by the capacity of health care and relevant covariates (such as demographic features and geographic areas) with the Coxian phase-type (CPH) process. We applied the proposed QH-CPH model to Taiwanese nationwide colorectal cancer screening program data for estimating the arrival rate and the probability of not complying with colonoscopy and classifying the compliers into two hidden states, short-waiting phase and long-waiting phase for colonoscopy. Significant covariates responsible for three processes were also identified by using the proportional hazards regression forms. A simulation study was further performed to assess the joint effect of these parameters on WT through a series of scenarios. The proposed QH-CPH model can provide an insight into the optimal and the practical design on population-based cancer screening for health policy-makers given the limited health care resources and capacity.
      PubDate: 2022-09-01
       
  • Correction to: On the equivalence of conglomerability and disintegrability
           for unbounded random variables

    • Free pre-print version: Loading...

      PubDate: 2022-08-03
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 44.192.26.60
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-