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  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: 149)
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  

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Similar Journals
Journal Cover
Computational Statistics
Journal Prestige (SJR): 0.803
Citation Impact (citeScore): 1
Number of Followers: 15  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1613-9658 - ISSN (Online) 0943-4062
Published by Springer-Verlag Homepage  [2469 journals]
  • Multivariate understanding of income and expenditure in United States
           households with statistical learning

    • Free pre-print version: Loading...

      Abstract: Abstract In recent decades, data-driven approaches have been developed to analyze demographic and economic surveys on a large scale. Despite advances in multivariate techniques and learning methods, in practice the analysis and interpretations are often focused on a small portion of available data and limited to a single perspective. This paper aims to utilize a selected array of multivariate statistical learning methods in the analysis of income and expenditure patterns of households in the United States using the Public-Use Microdata from the Bureau of Labor Statistics Consumer Expenditure Survey (CE). The objective is to propose an effective data pipeline that provides visualizations and comprehensive interpretations for applications in governmental regulations and economic research, using thirty-five original survey variables covering the categories of demographics, income and expenditure. Details on feature extraction not only showcase CE as a unique publicly-shared big data resource with high potential for in-depth analysis, but also assist interested researchers with pre-processing. Challenges from missing values and categorical variables are treated in the exploratory analysis, while statistical learning methods are comprehensively employed to address multiple economic perspectives. Principal component analysis suggests that after-tax income, wage/salary income, and the quarterly expenditure in food, housing and overall as the five most important of the selected variables, while cluster analysis identifies and visualizes the implicit structure between variables. Based on this, canonical correlation analysis reveals high correlation between two selected groups of variables, one of income and the other of expenditure.
      PubDate: 2022-11-01
       
  • Nomclust 2.0: an R package for hierarchical clustering of objects
           characterized by nominal variables

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      Abstract: Abstract In this paper, we present the second generation of the nomclust R package, which we developed for the hierarchical clustering of data containing nominal variables (nominal data). The package completely covers the hierarchical clustering process, from dissimilarity matrix calculation, over the choice of a clustering method, to the evaluation of the final clusters. Through the whole clustering process, similarity measures, clustering methods, and evaluation criteria developed solely for nominal data are used, which makes this package unique. In the first part of the paper, the theoretical background of the methods used in the package is described. In the second part, the functionality of the package is demonstrated in several examples. The second generation of the package is completely rewritten to be more natural for the workflow of R users. It includes new similarity measures and evaluation criteria. We also added several graphical outputs and support for S3 generic functions. Finally, due to code optimizations, the calculation time of dissimilarity matrix calculation was substantially reduced.
      PubDate: 2022-11-01
       
  • Non-parametric seasonal unit root tests under periodic non-stationary
           volatility

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      Abstract: Abstract This paper presents a new non-parametric seasonal unit root testing framework that is robust to periodic non-stationary volatility in innovation variance by making an extension to the fractional seasonal variance ratio unit root tests of Eroğlu et al. (Econ Lett 167:75–80, 2018). The setup allows for both periodic heteroskedasticity structure of Burridge and Taylar (J Econ 104(1):91–117, 2001) and non-stationary volatility structure of Cavaliere and Taylor (Econ Theory 24(1):43-71, 2008). We show that the limiting null distributions of the variance ratio tests depend on nuisance parameters derived from the underlying volatility process. Monte Carlo simulations show that the standard variance ratio tests can be substantially oversized in the presence of such effects. Consequently, we propose wild bootstrap implementations of the variance ratio tests. Wild bootstrap resampling schemes are shown to deliver asymptotically pivotal inference. The simulation evidence depicts that the proposed bootstrap tests perform well in practice and essentially correct the size problems observed in the standard fractional seasonal variance ratio tests, even under extreme patterns of heteroskedasticity.
      PubDate: 2022-11-01
       
  • Overcoming convergence problems in PLS path modelling

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      Abstract: Abstract The present paper deals with convergence issues of Lohmöller’s procedure for the computation of the components in the PLS-PM algorithm. More datasets and proofs are given to highlight the convergence failure of this procedure. Consequently, a new procedure based on the Signless Lapalacien matrix of the indirect graph between constructs is introduced. In several cases that will be specified in this paper, both monotony and error convergence for this new procedure will be established. Several comparisons will be presented between the new procedure and the two conventionally used procedures (Lohmöller’s and Hanafi-Wold’s procedures).
      PubDate: 2022-11-01
       
  • Oblique decision tree induction by cross-entropy optimization based on the
           von Mises–Fisher distribution

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      Abstract: Abstract Oblique decision trees recursively divide the feature space by using splits based on linear combinations of attributes. Compared to their univariate counterparts, which only use a single attribute per split, they are often smaller and more accurate. A common approach to learn decision trees is by iteratively introducing splits on a training set in a top–down manner, yet determining a single optimal oblique split is in general computationally intractable. Therefore, one has to rely on heuristics to find near-optimal splits. In this paper, we adapt the cross-entropy optimization method to tackle this problem. The approach is motivated geometrically by the observation that equivalent oblique splits can be interpreted as connected regions on a unit hypersphere which are defined by the samples in the training data. In each iteration, the algorithm samples multiple candidate solutions from this hypersphere using the von Mises–Fisher distribution which is parameterized by a mean direction and a concentration parameter. These parameters are then updated based on the best performing samples such that when the algorithm terminates a high probability mass is assigned to a region of near-optimal solutions. Our experimental results show that the proposed method is well-suited for the induction of compact and accurate oblique decision trees in a small amount of time.
      PubDate: 2022-11-01
       
  • Ensemble updating of categorical state vectors

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      Abstract: Abstract An ensemble updating method for categorical state vectors is proposed. The method is based on a Bayesian view of the ensemble Kalman filter (EnKF). In the EnKF, Gaussian approximations to the forecast and filtering distributions are introduced, and the forecast ensemble is updated with a linear shift. Given that the Gaussian approximation to the forecast distribution is correct, the EnKF linear update corresponds to conditional simulation from a Gaussian distribution with mean and covariance such that the posterior samples marginally are distributed according to the Gaussian approximation to the filtering distribution. In the proposed approach for categorical vectors, the Gaussian approximations are replaced with a (possibly higher order) Markov chain model, and the linear update is replaced with simulation based on a class of decomposable graphical models. To make the update robust against errors in the assumed forecast and filtering distributions, an optimality criterion is formulated, for which the resulting optimal updating procedure can be found by solving a linear program. We explore the properties of the proposed updating procedure in a simulation example where each state variable can take three values.
      PubDate: 2022-11-01
       
  • Regularized target encoding outperforms traditional methods in supervised
           machine learning with high cardinality features

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      Abstract: Abstract Since most machine learning (ML) algorithms are designed for numerical inputs, efficiently encoding categorical variables is a crucial aspect in data analysis. A common problem are high cardinality features, i.e. unordered categorical predictor variables with a high number of levels. We study techniques that yield numeric representations of categorical variables which can then be used in subsequent ML applications. We focus on the impact of these techniques on a subsequent algorithm’s predictive performance, and—if possible—derive best practices on when to use which technique. We conducted a large-scale benchmark experiment, where we compared different encoding strategies together with five ML algorithms (lasso, random forest, gradient boosting, k-nearest neighbors, support vector machine) using datasets from regression, binary- and multiclass–classification settings. In our study, regularized versions of target encoding (i.e. using target predictions based on the feature levels in the training set as a new numerical feature) consistently provided the best results. Traditionally widely used encodings that make unreasonable assumptions to map levels to integers (e.g. integer encoding) or to reduce the number of levels (possibly based on target information, e.g. leaf encoding) before creating binary indicator variables (one-hot or dummy encoding) were not as effective in comparison.
      PubDate: 2022-11-01
       
  • Improved confidence intervals based on ranked set sampling designs within
           a parametric bootstrap approach

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      Abstract: Abstract We study the problem of obtaining confidence intervals (CIs) within a parametric framework under different ranked set sampling (RSS) designs. This is an important research issue since it has not yet been adequately addressed in the RSS literature. We focused on evaluating CIs based on a recently developed parametric bootstrap approach, and the asymptotic maximum likelihood CIs under simple random sampling (SRS) was taken as the counterpart. A comprehensive simulation study was carried out to evaluate the accuracy and precision of the CIs. We have considered as sampling designs the paired RSS, neoteric RSS, and double RSS, besides the original RSS and SRS. Different estimation methods and bootstrap CIs were evaluated. In addition, the robustness of the CIs to imperfect ranking was evaluated by inducing varied levels of ranking errors. The simulated results allowed us to identify accurate bootstrap CIs based on RSS and some of its extensions, which outperform the usual asymptotic or bootstrap CIs based on SRS in terms of accuracy (coverage rate) and/or precision (average width).
      PubDate: 2022-11-01
       
  • The 2017 Data Challenge of the American Statistical Association

    • Free pre-print version: Loading...

      PubDate: 2022-11-01
       
  • Uniform design with prior information of factors under weighted
           wrap-around $$L_2$$ -discrepancy

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      Abstract: Abstract Uniform design is one of the most frequently used designs of experiment, and all factors are usually regarded as equally important in the existing literature of uniform design. If some prior information of certain factors is known, the potential importance of factors should be distinguished. In this paper, by assigning different weights to factors with different importance, the weighted wrap-around \(L_2\) -discrepancy is proposed to measure the uniformity of design when some prior information of certain factors are known. The properties of weighted wrap-around \(L_2\) -discrepancy are explored. Accordingly, the weighted generalized wordlength pattern is proposed to describe the aberration of these kinds of designs. The relationship between the weighted wrap-around \(L_2\) -discrepancy and weighted generalized wordlength pattern is built, and a lower bound of weighted wrap-around \(L_2\) -discrepancy is obtained. Numerical results show that both weighted wrap-around \(L_2\) -discrepancy and weighted generalized wordlength pattern are precisely to capture the difference of importance among the columns of design.
      PubDate: 2022-11-01
       
  • A variational inference for the Lévy adaptive regression with
           multiple kernels

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      Abstract: Abstract This paper presents a variational Bayes approach to a Lévy adaptive regression kernel (LARK) model that represents functions with an overcomplete system. In particular, we develop a variational inference method for a LARK model with multiple kernels (LARMuK) which estimates arbitrary functions that could have jump discontinuities. The algorithm is based on a variational Bayes approximation method with simulated annealing. We compare the proposed algorithm to a simulation-based reversible jump Markov chain Monte Carlo (RJMCMC) method using numerical experiments and discuss its potential and limitations.
      PubDate: 2022-11-01
       
  • Fast simulation of tempered stable Ornstein–Uhlenbeck processes

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      Abstract: Abstract Constructing Lévy-driven Ornstein–Uhlenbeck processes is a task closely related to the notion of self-decomposability. In particular, their transition laws are linked to the properties of what will be hereafter called the a-remainder of their self-decomposable stationary laws. In the present study we fully characterize the Lévy triplet of these \(a\) -remainders and we provide a general framework to deduce the transition laws of the finite variation Ornstein–Uhlenbeck processes associated with tempered stable distributions. We focus finally on the subclass of the exponentially-modulated tempered stable laws and we derive the algorithms for an exact generation of the skeleton of Ornstein–Uhlenbeck processes related to such distributions, with the further advantage of adopting procedures which are tens of times faster than those already available in the existing literature.
      PubDate: 2022-11-01
       
  • Optimal control for parameter estimation in partially observed
           hypoelliptic stochastic differential equations

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      Abstract: Abstract We deal with the problem of parameter estimation in stochastic differential equations (SDEs) in a partially observed framework. We aim to design a method working for both elliptic and hypoelliptic SDEs, the latters being characterized by degenerate diffusion coefficients. This feature often causes the failure of constrast estimator based on Euler Maruyama discretization scheme and dramatically impairs classic stochastic filtering methods used to reconstruct the unobserved states. All of theses issues make the estimation problem in hypoelliptic SDEs difficult to solve. To overcome this, we construct a well-defined cost function no matter the elliptic nature of the SDEs. We also bypass the filtering step by considering a control theory perspective. The unobserved states are estimated by solving deterministic optimal control problems using numerical methods which do not need strong assumptions on the diffusion coefficient conditioning. Numerical simulations made on different partially observed hypoelliptic SDEs reveal our method produces accurate estimate while dramatically reducing the computational price comparing to other estimation procedures.
      PubDate: 2022-11-01
       
  • Tuning selection for two-scale kernel density estimators

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      Abstract: Abstract Reducing the bias of kernel density estimators has been a classical topic in nonparametric statistics. Schucany and Sommers (1977) proposed a two-scale estimator which cancelled the lower order bias by subtracting an additional kernel density estimator with a different scale of bandwidth. Different from existing literatures that treat the scale parameter in the two-scale estimator as a static global parameter, in this paper we consider an adaptive scale (i.e., dependent on the data point) so that the theoretical mean squared error can be further reduced. Practically, both the bandwidth and the scale parameter would require tuning, using for example, cross validation. By minimizing the point-wise mean squared error, we derive an approximate equation for the optimal scale parameter, and correspondingly propose to determine the scale parameter by solving an estimated equation. As a result, the only parameter that requires tuning using cross validation is the bandwidth. Point-wise consistency of the proposed estimator for the optimal scale is established with further discussions. The promising performance of the two-scale estimator based on the adaptive variable scale is illustrated via numerical studies on density functions with different shapes.
      PubDate: 2022-11-01
       
  • Inference for copula-based dependent competing risks model with
           step-stress accelerated life test under generalized progressive hybrid
           censoring

    • Free pre-print version: Loading...

      Abstract: Abstract In this paper, a dependent competing risks model is considered and investigated by utilizing the copula approach which flexibly constructs dependent relationships between marginal distributions of several competing risk factors, and the closeness of the dependency can be determined by the copula parameters. Samples are collected from the step-stress accelerated life test combined with generalized progressive hybrid censoring, where the test duration is controlled within acceptable limits and a sufficient number of samples are guaranteed. From a classical frequency perspective, the maximum likelihood estimates are derived, then the relevant confidence intervals are provided based on the Fisher information matrix. Furthermore, the bias-corrected accelerated bootstrap method is employed to obtain the interval estimation of parameters. Under the Bayesian framework, Bayesian point estimates and the corresponding credible intervals are also explored, and their results are achieved by Markov Chain Monte Carlo technique. Finally, numerical simulation experiments and real data analysis are developed to more intuitively present the constructed model and the performance of the above-mentioned methods.
      PubDate: 2022-11-01
       
  • Robust sparse Bayesian infinite factor models

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      Abstract: Abstract Most of previous works and applications of Bayesian factor model have assumed the normal likelihood regardless of its validity. We propose a Bayesian factor model for heavy-tailed high-dimensional data based on multivariate Student-t likelihood to obtain better covariance estimation. We use multiplicative gamma process shrinkage prior and factor number adaptation scheme proposed in Bhattacharya and Dunson [Biometrika 98(2):291–306, 2011]. Since a naive Gibbs sampler for the proposed model suffers from slow mixing, we propose a Markov Chain Monte Carlo algorithm where fast mixing of Hamiltonian Monte Carlo is exploited for some parameters in the proposed model. Simulation results illustrate the gain in performance of covariance estimation for heavy-tailed high-dimensional data. We also provide a theoretical result that the posterior of the proposed model is weakly consistent under reasonable conditions. We conclude the paper with the application of the proposed factor model on breast cancer metastasis prediction given DNA signature data of cancer cells.
      PubDate: 2022-11-01
       
  • Household energy expenditure and consumption patterns in the United States

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      Abstract: Abstract Developing policies for a greener society calls for understanding the energy consumption patterns of its households. Using data from the Bureau of Labor Statistics 2015 Consumer Expenditure Survey and the U.S. Energy Information Administration, this article considers variations in energy expenditure and consumption patterns in the United States and seeks to determine if there is a relationship between a household’s energy expenditure and use patterns, and its sociodemographic characteristics. The study begins with a set of sociodemographic characteristics such as housing size, family size, number of cars, and education level, and uses cluster analysis to reduce these variables into a single categorical sociodemographic variable. Analyses of variance are then performed to study differences in energy consumption patterns among the clusters across the United States. Additionally, chi-square tests are applied to study associations between energy use with other defining variables such as geographic region and housing tenure. Notable findings include an economy of scaling when multiple people live together, larger energy demands of more isolated residences, and lower energy demands of urban blue-collar households. In the face of climate change, there has been growing interest in developing energy conservation goals. With this study, we seek to contribute to the discussion by investigating possible factors associated with certain energy use patterns.
      PubDate: 2022-11-01
       
  • Two sample tests for Semi-Markov processes with parametric sojourn time
           distributions: an application in sensory analysis

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      Abstract: Abstract Developing statistical approaches that are able to compare the probability law of qualitative trajectories can be of real interest in many fields of science such as economics and sociology, quality control or epidemiology. This work is motivated by an application in sensory analysis in which subjects indicate the succession of perceived sensations over time using a list of attributes. In Lecuelle (Food Qual Prefer 67:59–66, 2018), Semi-Markov Processes (SMPs) are introduced to model such data, allowing to take into account the dynamics via the transitions from one attribute to another as well as the duration law of each attribute. One of the major challenges of sensory analysis is to determine if two tasted products are perceived differently. For that purpose, the present paper introduces a statistical testing procedure based on the likelihood ratio between two semi-Markov processes, assuming a parametric form for the sojourn time distributions. Three approaches are evaluated to compute the p-value: a first one based on the asymptotic law of the likelihood ratio, a second one based on the parametric bootstrap and a third one based on permutations. These approaches are compared on Monte-Carlo simulated data both in terms of empirical levels under the null hypothesis and statistical powers under alternatives. We also develop partial tests to compare two processes on either their initial probabilities and transition matrices or their sojourn time distributions. Simulations show that permutation approaches perform better in nearly all situations and especially for small and moderate sample sizes. Finally, the proposed tests are illustrated on real datasets which consist in perceived sensations over time during the tasting of different chocolates and cheeses.
      PubDate: 2022-11-01
       
  • Adaptive step-length selection in gradient boosting for Gaussian location
           and scale models

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      Abstract: Abstract Tuning of model-based boosting algorithms relies mainly on the number of iterations, while the step-length is fixed at a predefined value. For complex models with several predictors such as Generalized additive models for location, scale and shape (GAMLSS), imbalanced updates of predictors, where some distribution parameters are updated more frequently than others, can be a problem that prevents some submodels to be appropriately fitted within a limited number of boosting iterations. We propose an approach using adaptive step-length (ASL) determination within a non-cyclical boosting algorithm for Gaussian location and scale models, as an important special case of the wider class of GAMLSS, to prevent such imbalance. Moreover, we discuss properties of the ASL and derive a semi-analytical form of the ASL that avoids manual selection of the search interval and numerical optimization to find the optimal step-length, and consequently improves computational efficiency. We show competitive behavior of the proposed approaches compared to penalized maximum likelihood and boosting with a fixed step-length for Gaussian location and scale models in two simulations and two applications, in particular for cases of large variance and/or more variables than observations. In addition, the underlying concept of the ASL is also applicable to the whole GAMLSS framework and to other models with more than one predictor like zero-inflated count models, and brings up insights into the choice of the reasonable defaults for the step-length in the simpler special case of (Gaussian) additive models.
      PubDate: 2022-11-01
       
  • A note on imputing squares via polynomial combination approach

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      Abstract: Abstract The polynomial combination (PC) method, proposed by Vink and Van Buuren, is a hot-deck multiple imputation method for imputation models containing squared terms. The method yields unbiased regression estimates and preserves the quadratic relationships in the imputed data for both MCAR and MAR mechanisms. However, Vink and Van Buuren never studied the coverage rate of the PC method. This paper investigates the coverage of the nominal 95% confidence intervals for the polynomial combination method and improves the algorithm to avoid the perfect prediction issue. We also compare the original and the improved PC method to the substantive model compatible fully conditional specification method proposed by Bartlett et al. and elucidate the two imputation methods’ characters.
      PubDate: 2022-11-01
       
 
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