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Multidimensional Stationary Time Series Dimension Reduction and PredictionMariannaBolla, TamásSzabadosRoutledge, 2023, xiv + 318 pages, $59.95, paperback ISBN: 9780367619701 Int. Stat. Rev. (IF 2.0) Pub Date : 2024-03-12 Brian W. Sloboda
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A Slicing‐Free Perspective to Sufficient Dimension Reduction: Selective Review and Recent Developments Int. Stat. Rev. (IF 2.0) Pub Date : 2024-03-07 Lu Li, Xiaofeng Shao, Zhou Yu
SummarySince the pioneering work of sliced inverse regression, sufficient dimension reduction has been growing into a mature field in statistics and it has broad applications to regression diagnostics, data visualisation, image processing and machine learning. In this paper, we provide a review of several popular inverse regression methods, including sliced inverse regression (SIR) method and principal
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Parametric Quantile Beta Regression Model Int. Stat. Rev. (IF 2.0) Pub Date : 2024-02-26 Marcelo Bourguignon, Diego I. Gallardo, Helton Saulo
SummaryIn this paper, we develop a fully parametric quantile regression model based on the generalised three‐parameter beta (GB3) distribution. Beta regression models are primarily used to model rates and proportions. However, these models are usually specified in terms of a conditional mean. Therefore, they may be inadequate if the observed response variable follows an asymmetrical distribution. In
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On the Inversion-Free Newton's Method and Its Applications Int. Stat. Rev. (IF 2.0) Pub Date : 2024-02-01 Huy N. Chau, J. Lars Kirkby, Dang H. Nguyen, Duy Nguyen, Nhu N. Nguyen, Thai Nguyen
In this paper, we survey the recent development of inversion-free Newton's method, which directly avoids computing the inversion of Hessian, and demonstrate its applications in estimating parameters of models such as linear and logistic regression. A detailed review of existing methodology is provided, along with comparisons of various competing algorithms. We provide numerical examples that highlight
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On the Most Effective Use of Continuous Auxiliary Variables in Regression Estimation in Survey Sampling Int. Stat. Rev. (IF 2.0) Pub Date : 2023-12-22 Takis Merkouris
Auxiliary variables with known population totals are extensively used in survey sampling to construct generalised regression (GR) estimators or optimal regression (OR) estimators of totals or means of study variables. This article explores the possibility of improving the efficiency of such estimators when continuous auxiliary variables are used in the regression estimation jointly with appropriate
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Issue Information Int. Stat. Rev. (IF 2.0) Pub Date : 2023-11-07
No abstract is available for this article.
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Matrix-Variate Time Series Analysis: A Brief Review and Some New Developments Int. Stat. Rev. (IF 2.0) Pub Date : 2023-11-02 Ruey S. Tsay
This paper briefly reviews the recent research in matrix-variate time series analysis, discusses some new developments, especially for seasonal time series, and demonstrates some applications. A general matrix autoregressive moving-average model is introduced. The paper narrates a simple approach for understanding the model, identifiability issues, and estimation. Real examples are used to illustrate
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Interview With Adrian Raftery Int. Stat. Rev. (IF 2.0) Pub Date : 2023-10-25 Leontine Alkema, Thomas Brendan Murphy, Adrian E. Raftery
Professor Adrian E. Raftery is the Boeing International Professor of Statistics and Sociology and an adjunct professor of Atmospheric Sciences at the University of Washington in Seattle. He was born in Dublin, Ireland, and obtained a BA in Mathematics (1976) and an MSc in Statistics and Operations Research (1977) at Trinity College Dublin. He obtained a doctorate in mathematical statistics in 1980
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New Randomised Response Models for Two Sensitive Characteristics: Theory and Application Int. Stat. Rev. (IF 2.0) Pub Date : 2023-10-23 Daryan Naatjes, Stephen A. Sedory, Sarjinder Singh
In this paper, we introduce two new randomised response models for estimating the prevalence of two sensitive characteristics and their overlap in a population by making use of a single deck of cards. The proposed models ensure the privacy of the respondents and also reduce the burden on the respondents as they require the random selection of only one card from a deck of cards each of which contains
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A Spatial Variance-Smoothing Area Level Model for Small Area Estimation of Demographic Rates Int. Stat. Rev. (IF 2.0) Pub Date : 2023-10-17 Peter A. Gao, Jonathan Wakefield
Accurate estimates of subnational health and demographic indicators are critical for informing policy. Many countries collect relevant data using complex household surveys, but when data are limited, direct weighted estimates of small area proportions may be unreliable. Area level models treating these direct estimates as response data can improve precision but often require known sampling variances
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Estimation of Graphical Models: An Overview of Selected Topics Int. Stat. Rev. (IF 2.0) Pub Date : 2023-10-04 Li-Pang Chen
Graphical modelling is an important branch of statistics that has been successfully applied in biology, social science, causal inference and so on. Graphical models illuminate connections between many variables and can even describe complex data structures or noisy data. Graphical models have been combined with supervised learning techniques such as regression modelling and classification analysis
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A Review of Data-Driven Discovery for Dynamic Systems Int. Stat. Rev. (IF 2.0) Pub Date : 2023-09-29 Joshua S. North, Christopher K. Wikle, Erin M. Schliep
Many real-world scientific processes are governed by complex non-linear dynamic systems that can be represented by differential equations. Recently, there has been an increased interest in learning, or discovering, the forms of the equations driving these complex non-linear dynamic systems using data-driven approaches. In this paper, we review the current literature on data-driven discovery for dynamic
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Penalisation Methods in Fitting High-Dimensional Cointegrated Vector Autoregressive Models: A Review Int. Stat. Rev. (IF 2.0) Pub Date : 2023-09-19 Marie Levakova, Susanne Ditlevsen
Cointegration has shown useful for modeling non-stationary data with long-run equilibrium relationships among variables, with applications in many fields such as econometrics, climate research and biology. However, the analyses of vector autoregressive models are becoming more difficult as data sets of higher dimensions are becoming available, in particular because the number of parameters is quadratic
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Generalised Income Inequality Index Int. Stat. Rev. (IF 2.0) Pub Date : 2023-08-07 Ziqing Dong, Yves Tille, Giovanni Maria Giorgi, Alessio Guandalini
This paper proposes a deep generalisation for income inequality indices. A generalised income inequality index that depends on two parameters and that involves a large set of income inequality indices in the same framework is proposed. The two parameters control the sensitivity of the generalised index to different levels of the income distribution. A thorough investigation of the generalised index
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Issue Information Int. Stat. Rev. (IF 2.0) Pub Date : 2023-08-07
No abstract is available for this article.
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Hybrid SV-GARCH, t-GARCH and Markov-switching covariance structures in VEC models—Which is better from a predictive perspective? Int. Stat. Rev. (IF 2.0) Pub Date : 2023-06-29 Anna Pajor, Justyna Wróblewska, Łukasz Kwiatkowski, Jacek Osiewalski
We compare predictive performance of a multitude of alternative Bayesian vector autoregression (VAR) models allowing for cointegration and time-varying conditional covariances, described by different multivariate stochastic volatility (MSV) models, including their hybrids with multivariate GARCH processes (MSV-MGARCH), as well as t-GARCH and Markov-switching structures. The forecast accuracy is evaluated
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Online Evidential Nearest Neighbour Classification for Internet of Things Time Series Int. Stat. Rev. (IF 2.0) Pub Date : 2023-05-24 Patrick Toman, Nalini Ravishanker, Sanguthevar Rajasekaran, Nathan Lally
The ‘Internet of Things’ (IoT) is a rapidly developing set of technologies that leverages large numbers of networked sensors, to relay data in an online fashion. Typically, knowledge of the sensor environment is incomplete and subject to changes over time. There is a need to employ classification algorithms to understand the data. We first review of existing time series classification (TSC) approaches
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Increasing Trust in New Data Sources: Crowdsourcing Image Classification for Ecology Int. Stat. Rev. (IF 2.0) Pub Date : 2023-05-21 Edgar Santos-Fernandez, Julie Vercelloni, Aiden Price, Grace Heron, Bryce Christensen, Erin E. Peterson, Kerrie Mengersen
Crowdsourcing methods facilitate the production of scientific information by non-experts. This form of citizen science (CS) is becoming a key source of complementary data in many fields to inform data-driven decisions and study challenging problems. However, concerns about the validity of these data often constrain their utility. In this paper, we focus on the use of citizen science data in addressing
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Correspondence Analysis Using the Cressie–Read Family of Divergence Statistics Int. Stat. Rev. (IF 2.0) Pub Date : 2023-05-15 Eric J. Beh, Rosaria Lombardo
The foundations of correspondence analysis rests with Pearson's chi-squared statistic. More recently, it has been shown that the Freeman–Tukey statistic plays an important role in correspondence analysis and confirmed the advantages of the Hellinger distance that have long been advocated in the literature. Pearson's and the Freeman–Tukey statistics are two of five commonly used special cases of the
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Issue Information Int. Stat. Rev. (IF 2.0) Pub Date : 2023-04-03
No abstract is available for this article.
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An interview with Luis Raúl Pericchi Int. Stat. Rev. (IF 2.0) Pub Date : 2023-03-21 Abel Rodríguez, Bruno Sansó
Luis Raúl Pericchi Guerra was born in Caracas, Venezuela, on 11 March 1952. He completed a B.S. in Mathematics in 1975 at the Universidad Simón Bolívar in Caracas, an M.S. in Statistics at the University of California Berkeley in 1978 and a Ph.D. in Statistics at Imperial College London in 1981. After graduating from Imperial College, Luis Raúl went back to Universidad Simón Bolívar. There, he played
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Estimating the Reciprocal of a Binomial Proportion Int. Stat. Rev. (IF 2.0) Pub Date : 2023-03-20 Jiajin Wei, Ping He, Tiejun Tong
The binomial proportion is a classic parameter with many applications and has also been extensively studied in the literature. By contrast, the reciprocal of the binomial proportion, or the inverse proportion, is often overlooked, even though it also plays an important role in various fields. To estimate the inverse proportion, the maximum likelihood method fails to yield a valid estimate when there
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Optimal Treatment Regimes: A Review and Empirical Comparison Int. Stat. Rev. (IF 2.0) Pub Date : 2023-02-22 Zhen Li, Jie Chen, Eric Laber, Fang Liu, Richard Baumgartner
A treatment regime is a sequence of decision rules, one per decision point, that maps accumulated patient information to a recommended intervention. An optimal treatment regime maximises expected cumulative utility if applied to select interventions in a population of interest. As a treatment regime seeks to improve the quality of healthcare by individualising treatment, it can be viewed as an approach
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A Statistical Review of Template Model Builder: A Flexible Tool for Spatial Modelling Int. Stat. Rev. (IF 2.0) Pub Date : 2022-12-18 Aaron Osgood-Zimmerman, Jon Wakefield
The integrated nested Laplace approximation (INLA) is a well-known and popular technique for spatial modelling with a user-friendly interface in the R-INLA package. Unfortunately, only a certain class of latent Gaussian models are amenable to fitting with INLA. In this paper, we review template model builder (TMB), an existing technique and software package which is well-suited to fitting complex spatio-temporal
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Improving Probabilistic Record Linkage Using Statistical Prediction Models Int. Stat. Rev. (IF 2.0) Pub Date : 2022-12-04 Angelo Moretti, Natalie Shlomo
Record linkage brings together information from records in two or more data sources that are believed to belong to the same statistical unit based on a common set of matching variables. Matching variables, however, can appear with errors and variations and the challenge is to link statistical units that are subject to error. We provide an overview of record linkage techniques and specifically investigate
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A joint normal-binary (probit) model Int. Stat. Rev. (IF 2.0) Pub Date : 2022-11-08 Margaux Delporte, Steffen Fieuws, Geert Molenberghs, Geert Verbeke, Simeon Situma Wanyama, Elpis Hatziagorou, Christiane De Boeck
In biomedical research, often hierarchical binary and continuous responses need to be jointly modelled. In joint generalised linear mixed models, this can be done with correlated random effects, which allows examining the association structure between the various responses and the evolution of this association over time. In addition, the effect of covariates on all outcomes can be assessed simultaneously
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Issue Information Int. Stat. Rev. (IF 2.0) Pub Date : 2022-11-03
No abstract is available for this article.
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Likelihood-Based Inference for the Finite Population Mean with Post-Stratification Information Under Non-Ignorable Non-Response Int. Stat. Rev. (IF 2.0) Pub Date : 2022-10-25 Sahar Z. Zangeneh, Roderick J. Little
We describe models and likelihood-based estimation of the finite population mean for a survey subject to unit non-response, when post-stratification information is available from external sources. A feature of the models is that they do not require the assumption that the data are missing at random (MAR). As a result, the proposed models provide estimates under weaker assumptions than those required
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Global seasonal and pandemic patterns in influenza: An application of longitudinal study designs Int. Stat. Rev. (IF 2.0) Pub Date : 2022-10-23 Elena N. Naumova, Ryan B. Simpson, Bingjie Zhou, Meghan A. Hartwick
The confluence of growing analytic capacities and global surveillance systems for seasonal infections has created new opportunities to further develop statistical methodology and advance the understanding of the global disease dynamics. We developed a framework to characterise the seasonality of infectious diseases for publicly available global health surveillance data. Specifically, we aimed to estimate
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Synergy of Biostatistics and Epidemiology in Air Pollution Health Effects Studies Int. Stat. Rev. (IF 2.0) Pub Date : 2022-10-21 Douglas W. Dockery
The extraordinary advances in quantifying the health effects of ambient air pollution over the last five decades have led to dramatic improvement in air quality in the United States. This work has been possible through innovative epidemiologic study designs coupled with advanced statistical analytic methods. This paper presents a historical perspective on the coordinated developments of epidemiologic
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Accounting for Non-ignorable Sampling and Non-response in Statistical Matching Int. Stat. Rev. (IF 2.0) Pub Date : 2022-10-19 Daniela Marella, Danny Pfeffermann
Data for statistical analysis is often available from different samples, with each sample containing measurements on only some of the variables of interest. Statistical matching attempts to generate a fused database containing matched measurements on all the target variables. In this article, we consider the use of statistical matching when the samples are drawn by informative sampling designs and
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Path algorithms for fused lasso signal approximator with application to COVID-19 spread in Korea Int. Stat. Rev. (IF 2.0) Pub Date : 2022-10-19 Won Son, Johan Lim, Donghyeon Yu
The fused lasso signal approximator (FLSA) is a smoothing procedure for noisy observations that uses fused lasso penalty on unobserved mean levels to find sparse signal blocks. Several path algorithms have been developed to obtain the whole solution path of the FLSA. However, it is known that the FLSA has model selection inconsistency when the underlying signals have a stair-case block, where three
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A Bootstrap Variance Procedure for the Generalised Regression Estimator Int. Stat. Rev. (IF 2.0) Pub Date : 2022-10-19 Marius Stefan, Michael A. Hidiroglou
The generalised regression estimator (GREG) uses auxiliary data that are available from the finite population to improve the efficiency of the estimator of a total (mean). Estimators of the variance of GREG that have been proposed in the sampling literature include those based on Taylor linearisation and the jackknife techniques. Approximations based on Taylor expansions are reasonable for large samples
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Statistical analysis of longitudinal studies Int. Stat. Rev. (IF 2.0) Pub Date : 2022-10-17 Nan M. Laird
Longitudinal studies play a prominent role in research on growth, change and/or decline in individuals, and in characterising the environmental and social factors which influence change. The essential feature of a longitudinal study is taking repeated measures of an outcome on the same set of individuals at multiple timepoints, thereby allowing investigators to characterise within subject changes during
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ABC of the future Int. Stat. Rev. (IF 2.0) Pub Date : 2022-10-17 Henri Pesonen, Umberto Simola, Alvaro Köhn-Luque, Henri Vuollekoski, Xiaoran Lai, Arnoldo Frigessi, Samuel Kaski, David T. Frazier, Worapree Maneesoonthorn, Gael M. Martin, Jukka Corander
Approximate Bayesian computation (ABC) has advanced in two decades from a seminal idea to a practically applicable inference tool for simulator-based statistical models, which are becoming increasingly popular in many research domains. The computational feasibility of ABC for practical applications has been recently boosted by adopting techniques from machine learning to build surrogate models for
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A Legacy of EM Algorithms Int. Stat. Rev. (IF 2.0) Pub Date : 2022-10-12 Kenneth Lange, Hua Zhou
Nan Laird has an enormous and growing impact on computational statistics. Her paper with Dempster and Rubin on the expectation-maximisation (EM) algorithm is the second most cited paper in statistics. Her papers and book on longitudinal modelling are nearly as impressive. In this brief survey, we revisit the derivation of some of her most useful algorithms from the perspective of the minorisation-maximisation
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Simultaneous inference for linear mixed model parameters with an application to small area estimation Int. Stat. Rev. (IF 2.0) Pub Date : 2022-09-18 Katarzyna Reluga, María-José Lombardía, Stefan Sperlich
Over the past decades, linear mixed models have attracted considerable attention in various fields of applied statistics. They are popular whenever clustered, hierarchical or longitudinal data are investigated. Nonetheless, statistical tools for valid simultaneous inference for mixed parameters are rare. This is surprising because one often faces inferential problems beyond the pointwise examination
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A Computational Perspective on Projection Pursuit in High Dimensions: Feasible or Infeasible Feature Extraction Int. Stat. Rev. (IF 2.0) Pub Date : 2022-08-19 Chunming Zhang, Jimin Ye, Xiaomei Wang
Finding a suitable representation of multivariate data is fundamental in many scientific disciplines. Projection pursuit ( PP) aims to extract interesting ‘non-Gaussian’ features from multivariate data, and tends to be computationally intensive even when applied to data of low dimension. In high-dimensional settings, a recent work (Bickel et al., 2018) on PP addresses asymptotic characterization and
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Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference Int. Stat. Rev. (IF 2.0) Pub Date : 2022-08-11 Shixiao Zhang, Peisong Han, Changbao Wu
We provide a critical review on calibration methods developed in three different areas: survey sampling, missing data analysis and causal inference. We highlight the connections and variations of calibration techniques used in missing data analysis and causal inference to conventional calibration weighting and estimation in survey sampling and provide a common framework through model-calibration and
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Issue Information Int. Stat. Rev. (IF 2.0) Pub Date : 2022-08-04
No abstract is available for this article.
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Are You All Normal? It Depends! Int. Stat. Rev. (IF 2.0) Pub Date : 2022-07-07 Wanfang Chen, Marc G. Genton
The assumption of normality has underlain much of the development of statistics, including spatial statistics, and many tests have been proposed. In this work, we focus on the multivariate setting and first review the recent advances in multivariate normality tests for i.i.d. data, with emphasis on the skewness and kurtosis approaches. We show through simulation studies that some of these tests cannot
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Survival Modelling for Data From Combined Cohorts: Opening the Door to Meta Survival Analyses and Survival Analysis Using Electronic Health Records Int. Stat. Rev. (IF 2.0) Pub Date : 2022-06-16 James H. McVittie, Ana F. Best, David B. Wolfson, David A. Stephens, Julian Wolfson, David L. Buckeridge, Shahinaz M. Gadalla
Non-parametric estimation of the survival function using observed failure time data depends on the underlying data generating mechanism, including the ways in which the data may be censored and/or truncated. For data arising from a single source or collected from a single cohort, a wide range of estimators have been proposed and compared in the literature. Often, however, it may be possible, and indeed
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Scalable Bayesian Multiple Changepoint Detection via Auxiliary Uniformisation Int. Stat. Rev. (IF 2.0) Pub Date : 2022-06-15 Lu Shaochuan
In this paper, we perform a sparse filtering recursion for efficient changepoint detection for discrete-time observations. We attach auxiliary event times to the chronologically ordered observations and formulate multiple changepoint problems of discrete-time observations into continuous-time observations. Ideally, both the computational and memory costs of the proposed auxiliary uniformisation forward-filtering
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Diagnostic Tests for the Necessity of Weight in Regression With Survey Data Int. Stat. Rev. (IF 2.0) Pub Date : 2022-06-09 Feng Wang, HaiYing Wang, Jun Yan
To weight or not to weight in regression analyses with survey data has been debated in the literature. The problem is essentially a tradeoff between the bias and the variance of the regression coefficient estimator. An array of diagnostic tests for informative weights have been developed. Nonetheless, studies comparing the performance of the tests, especially for finite samples, are scarce, and the
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From Pareto to Weibull – A Constructive Review of Distributions on ℝ+ Int. Stat. Rev. (IF 2.0) Pub Date : 2022-06-06 Corinne Sinner, Yves Dominicy, Julien Trufin, Wout Waterschoot, Patrick Weber, Christophe Ley
Power laws and power laws with exponential cut-off are two distinct families of distributions on the positive real half-line. In the present paper, we propose a unified treatment of both families by building a family of distributions that interpolates between them, which we call Interpolating Family (IF) of distributions. Our original construction, which relies on techniques from statistical physics
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Using Survey Sampling Algorithms For Exact Inference in Logistic Regression Int. Stat. Rev. (IF 2.0) Pub Date : 2022-05-31 Louis-Paul Rivest, Serigne Abib Gaye
Several exact inference procedures for logistic regression require the simulation of a 0-1 dependent vector according to its conditional distribution, given the sufficient statistics for some nuisance parameters. This is viewed, in this work, as a sampling problem involving a population of n units, unequal selection probabilities and balancing constraints. The basis for this reformulation of exact
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Nonparametric Testing of the Dependence Structure Among Points–Marks–Covariates in Spatial Point Patterns Int. Stat. Rev. (IF 2.0) Pub Date : 2022-05-16 Jiří Dvořák, Tomáš Mrkvička, Jorge Mateu, Jonatan A. González
We investigate testing of the hypothesis of independence between a covariate and the marks in a marked point process. It would be rather straightforward if the (unmarked) point process were independent of the covariate and the marks. In practice, however, such an assumption is questionable and possible dependence between the point process and the covariate or the marks may lead to incorrect conclusions
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Priors in Bayesian Deep Learning: A Review Int. Stat. Rev. (IF 2.0) Pub Date : 2022-05-11 Vincent Fortuin
While the choice of prior is one of the most critical parts of the Bayesian inference workflow, recent Bayesian deep learning models have often fallen back on vague priors, such as standard Gaussians. In this review, we highlight the importance of prior choices for Bayesian deep learning and present an overview of different priors that have been proposed for (deep) Gaussian processes, variational autoencoders
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Elaboration Models with Symmetric Information Divergence Int. Stat. Rev. (IF 2.0) Pub Date : 2022-04-20 Majid Asadi, Karthik Devarajan, Nader Ebrahimi, Ehsan Soofi, Lauren Spirko-Burns
Various statistical methodologies embed a probability distribution in a more flexible family of distributions. The latter is called elaboration model, which is constructed by choice or a formal procedure and evaluated by asymmetric measures such as the likelihood ratio and Kullback–Leibler information. The use of asymmetric measures can be problematic for this purpose. This paper introduces two formal
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Should We Condition on the Number of Points When Modelling Spatial Point Patterns? Int. Stat. Rev. (IF 2.0) Pub Date : 2022-04-11 Jesper Møller, Ninna Vihrs
We discuss the practice of directly or indirectly assuming a model for the number of points when modelling spatial point patterns even though it is rarely possible to validate such a model in practice because most point pattern data consist of only one pattern. We therefore explore the possibility to condition on the number of points instead when fitting and validating spatial point process models
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Bias, Fairness and Accountability with Artificial Intelligence and Machine Learning Algorithms Int. Stat. Rev. (IF 2.0) Pub Date : 2022-04-10 Nengfeng Zhou, Zach Zhang, Vijayan N. Nair, Harsh Singhal, Jie Chen
The advent of artificial intelligence (AI) and machine learning algorithms has led to opportunities as well as challenges in their use. In this overview paper, we begin with a discussion of bias and fairness issues that arise with the use of AI techniques, with a focus on supervised machine learning algorithms. We then describe the types and sources of data bias and discuss the nature of algorithmic
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Some Solutions Inspired by Survey Sampling Theory to Build Effective Clinical Trials Int. Stat. Rev. (IF 2.0) Pub Date : 2022-04-10 Yves Tillé
The organisation of a design of experiments, for example, for the realisation of a clinical trial, is crucial. It is often desirable to balance designs so that the means of the covariates are approximately the same in the test and control groups. In survey sampling theory, balanced sampling and calibration are two techniques that improve the precision of estimates. In this paper, we show the links
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Rethinking the Effective Sample Size Int. Stat. Rev. (IF 2.0) Pub Date : 2022-04-10 Víctor Elvira, Luca Martino, Christian P. Robert
The effective sample size (ESS) is widely used in sample-based simulation methods for assessing the quality of a Monte Carlo approximation of a given distribution and of related integrals. In this paper, we revisit the approximation of the ESS in the specific context of importance sampling. The derivation of this approximation, that we will denote as ESS^, is partially available in a 1992 foundational
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Issue Information Int. Stat. Rev. (IF 2.0) Pub Date : 2022-04-03
No abstract is available for this article.
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Communicating with Data: The Art of Writing for Data ScienceDeborahNolan and Sara Stoudt Oxford University Press, 2021, vii + 331 pages, $45.95, paperback ISBN: 978‐0‐1988‐6275‐8 Int. Stat. Rev. (IF 2.0) Pub Date : 2022-03-15 Kelly McConville
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Replication and Evidence Factors in Observational StudiesPaul R.RosenbaumChapman & Hall/CRC, 2021, xviii + 254 pages, $120, hardback ISBN: 978‐036748‐388‐3 Int. Stat. Rev. (IF 2.0) Pub Date : 2022-03-14 John H. Maindonald
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Fundamentals of Causal Inference with RBabette A.BrumbackChapman & Hall/CRC, 2021, xi + 236 pages, $69.95, hardcover ISBN: 978‐0‐3677‐0505‐3 Int. Stat. Rev. (IF 2.0) Pub Date : 2022-03-13 Debashis Ghosh
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Population Genomics with REmmanuelParadisChapman & Hall/CRC, 2020, 394 pages, $120, hardback ISBN: 978‐1‐1386‐0818‐4 Int. Stat. Rev. (IF 2.0) Pub Date : 2022-03-13 Daniel Fischer