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  • An approximate Bayesian inference on propensity score estimation under unit nonresponse
    Can. J. Stat. (IF 0.656) Pub Date : 2021-01-11
    Hejian Sang; Jae Kwang Kim

    Nonresponse weighting adjustment using the response propensity score is a popular tool for handling unit nonresponse. Statistical inference after the nonresponse weighting adjustment is an important problem and Taylor linearization method is often used to reflect the effect of estimating the propensity score weights. In this article, we propose an approximate Bayesian approach to handle unit nonresponse

    更新日期:2021-01-11
  • Automatic sparse principal component analysis
    Can. J. Stat. (IF 0.656) Pub Date : 2020-12-20
    Heewon Park; Rui Yamaguchi; Seiya Imoto; Satoru Miyano

    The wide availability of computers enables us to accumulate a huge amount of data, thus effective tools to extract information from the huge volume of data have become critical. Principal component analysis (PCA) is a useful and traditional tool for dimensionality reduction of massive high‐dimensional datasets. Recently, sparse principal component (PC) loading estimation based on L1‐type regularization

    更新日期:2020-12-21
  • On logistic Box–Cox regression for flexibly estimating the shape and strength of exposure‐disease relationships
    Can. J. Stat. (IF 0.656) Pub Date : 2020-12-17
    Li Xing; Xuekui Zhang; Igor Burstyn; Paul Gustafson

    The shape of the relationship between a continuous exposure variable and a binary disease variable is often central to epidemiologic investigations. This article investigates a number of issues surrounding inference and the shape of the relationship. Presuming that the relationship can be expressed in terms of regression coefficients and a shape parameter, we investigate how well the shape can be inferred

    更新日期:2020-12-17
  • On uncertainty estimation in functional linear mixed models
    Can. J. Stat. (IF 0.656) Pub Date : 2020-11-26
    Tapabrata Maiti; Abolfazl Safikhani; Ping‐Shou Zhong

    Functional data analysis has proven useful in many scientific applications where a physical process is observed as a curve. In many applications, several curves are observed due to multiple subjects, providing replicates in the statistical sense. Recent literature develops several techniques for registering curves and estimating associated models in a regression framework. Standard regression models

    更新日期:2020-11-27
  • Evaluation of competing risks prediction models using polytomous discrimination index
    Can. J. Stat. (IF 0.656) Pub Date : 2020-11-20
    Maomao Ding; Jing Ning; Ruosha Li

    For competing risks data, it is often important to predict a patient's outcome status at a clinically meaningful time point after incorporating the informative censoring due to competing risks. This can be done by adopting a regression model that relates the cumulative incidence probabilities to a set of covariates. To assess the performance of the resulting prediction tool, we propose an estimator

    更新日期:2020-11-21
  • On set‐based association tests: Insights from a regression using summary statistics
    Can. J. Stat. (IF 0.656) Pub Date : 2020-11-20
    Yanyan Zhao; Lei Sun

    Motivated by, but not limited to, association analyses of multiple genetic variants, we propose here a summary statistics‐based regression framework. The proposed method requires only variant‐specific summary statistics, and it unifies earlier methods based on individual‐level data as special cases. The resulting score test statistic, derived from a linear mixed‐effect regression model, inherently

    更新日期:2020-11-21
  • A semiparametric regression model under biased sampling and random censoring: A local pseudo‐likelihood approach
    Can. J. Stat. (IF 0.656) Pub Date : 2020-11-19
    Yassir Rabhi; Masoud Asgharian

    Methodologies developed for left‐truncated right‐censored failure time data can mostly be categorized according to the assumption imposed on the truncation distribution, i.e., being completely unknown or completely known. While the former approach enjoys robustness, the latter is more efficient when the assumed form of the truncation distribution can be supported by the data. Motivated by data from

    更新日期:2020-11-19
  • Flexible Bayesian quantile curve fitting with shape restrictions under the Dirichlet process mixture of the generalized asymmetric Laplace distribution
    Can. J. Stat. (IF 0.656) Pub Date : 2020-11-04
    Genya Kobayashi; Taeyoung Roh; Jangwon Lee; Taeryon Choi

    We propose a flexible Bayesian semiparametric quantile regression model based on Dirichlet process mixtures of generalized asymmetric Laplace distributions for fitting curves with shape restrictions. The generalized asymmetric Laplace distribution exhibits more flexible tail behaviour than the frequently used asymmetric Laplace distribution in Bayesian quantile regression. In addition, nonparametric

    更新日期:2020-11-04
  • Quantile association regression on bivariate survival data
    Can. J. Stat. (IF 0.656) Pub Date : 2020-11-01
    Ling‐Wan Chen; Yu Cheng; Ying Ding; Ruosha Li

    The association between two event times is of scientific importance in various fields. Due to population heterogeneity, it is desirable to examine the degree to which local association depends on different characteristics of the population. Here we adopt a novel quantile‐based local association measure and propose a conditional quantile association regression model to allow covariate effects on local

    更新日期:2020-11-02
  • Sets that maximize probability and a related variational problem
    Can. J. Stat. (IF 0.656) Pub Date : 2020-11-01
    Juan J. Salamanca

    Let 𝒳 be a random variable of a Riemannian manifold. We assume that the C2‐probability density function of 𝒳 exists. This research addresses two variational questions. The first concerns sets that maximize their probability among those that have a fixed volume. We prove that such a set must have a probability density function that is constant along its boundary; equivalently, such a set must be a

    更新日期:2020-11-01
  • Semiparametric isotonic regression modelling and estimation for group testing data
    Can. J. Stat. (IF 0.656) Pub Date : 2020-10-28
    Ao Yuan; Jin Piao; Jing Ning; Jing Qin

    In the group testing procedure, several individual samples are grouped and the pooled samples, instead of each individual sample, are tested for outcome status (e.g., infectious disease status). Although this cost‐effectiveness strategy in data collection is both labour and time‐efficient, it poses statistical challenges to derive statistically and computationally efficient estimators under semiparametric

    更新日期:2020-10-30
  • Minimum Lq‐distance estimators for non‐normalized parametric models
    Can. J. Stat. (IF 0.656) Pub Date : 2020-10-28
    Steffen Betsch; Bruno Ebner; Bernhard Klar

    We propose and investigate a new estimation method for the parameters of models consisting of smooth density functions on the positive half axis. The procedure is based on a recently introduced characterization result for the respective probability distributions, and is to be classified as a minimum distance estimator, incorporating as a distance function the Lq‐norm. Throughout, we deal rigorously

    更新日期:2020-10-30
  • A broad class of zero‐or‐one inflated regression models for rates and proportions
    Can. J. Stat. (IF 0.656) Pub Date : 2020-10-22
    Francisco F. Queiroz; Artur J. Lemonte

    We introduce a family of distributions with bounded support for continuous rates or proportions when the data contain zeros or ones. On the basis of this class of distributions, we propose a novel class of regression models which is useful for modelling fractional data observed on [0, 1) or (0, 1]. The response variable of the new class of regression models has a mixed continuous‐discrete distribution

    更新日期:2020-10-26
  • Sure joint feature screening in nonparametric transformation model for right censored data
    Can. J. Stat. (IF 0.656) Pub Date : 2020-10-21
    Yi Liu; Jinfeng Xu; Gang Li

    Existing screening procedures for right censored data either posit a specific model or adopt a marginal approach; hence, they are prone to model misspecification or erroneous screening. To address these problems, we develop a joint feature screening method in nonparametric transformation models for censored survival data. A sparsity‐restricted estimator is proposed using a smoothed partial rank objective

    更新日期:2020-10-22
  • Local structure graph models with higher‐order dependence
    Can. J. Stat. (IF 0.656) Pub Date : 2020-10-21
    Emily M. Casleton; Daniel J. Nordman; Mark S. Kaiser

    Local structure graph models (LSGMs) describe random graphs and networks as a Markov random field (MRF)—each graph edge has a specified conditional distribution dependent on explicit neighbourhoods of other graph edges. Centered parameterizations of LSGMs allow for direct control and interpretation of parameters for large‐ and small‐scale structures (e.g., marginal means vs. dependence). We extend

    更新日期:2020-10-22
  • An efficient algorithm for Elastic I‐optimal design of generalized linear models
    Can. J. Stat. (IF 0.656) Pub Date : 2020-10-10
    Yiou Li; Xinwei Deng

    The generalized linear models (GLMs) are widely used in statistical analysis and the related design issues are undoubtedly challenging. The state‐of‐the‐art works mostly apply to design criteria on the estimates of regression coefficients. The prediction accuracy is usually critical in modern decision‐making and artificial intelligence applications. It is of importance to study optimal designs from

    更新日期:2020-10-11
  • Efficient nonparametric estimation for skewed distributions
    Can. J. Stat. (IF 0.656) Pub Date : 2020-10-10
    Cyril Favre‐Martinoz; David Haziza; Jean‐François Beaumont

    Many variables encountered in practice have skewed distributions. While the sample mean is unbiased for the true mean regardless of the underlying distribution that generated the sample observations, it can be highly unstable in the context of skewed distributions. To cope with this problem, we propose an efficient estimator of the population mean based on the concept of conditional bias of a unit

    更新日期:2020-10-11
  • Variable selection for proportional hazards models with high‐dimensional covariates subject to measurement error
    Can. J. Stat. (IF 0.656) Pub Date : 2020-08-31
    Baojiang Chen; Ao Yuan; Grace Y. Yi

    Methods of analyzing survival data with high‐dimensional covariates are often challenged by the presence of measurement error in covariates, a common issue arising from various applications. Conducting naive analysis with measurement‐error effects ignored usually gives biased results. However, relatively little research has been focused on this topic. In this article, we consider this important problem

    更新日期:2020-08-31
  • A Gaussian alternative to using improper confidence intervals
    Can. J. Stat. (IF 0.656) Pub Date : 2020-08-29
    André Plante

    The problem posed by exact confidence intervals (CIs) which can be either all‐inclusive or empty for a nonnegligible set of sample points is known to have no solution within CI theory. Confidence belts causing improper CIs can be modified by using margins of error from the renewed theory of errors initiated by J. W. Tukey—briefly described in the article—for which an extended Fraser's frequency interpretation

    更新日期:2020-08-29
  • Robust estimation of mean squared prediction error in small‐area estimation
    Can. J. Stat. (IF 0.656) Pub Date : 2020-08-26
    Ping Wu; Jiming Jiang

    The nested‐error regression model is one of the best‐known models in small area estimation. A small area mean is often expressed as a linear combination of fixed effects and realized values of random effects. In such analyses, prediction is made by borrowing strength from other related areas or sources and mean‐squared prediction error (MSPE) is often used as a measure of uncertainty. In this article

    更新日期:2020-08-27
  • Weighted Bayesian bootstrap for scalable posterior distributions
    Can. J. Stat. (IF 0.656) Pub Date : 2020-08-20
    Michael A. Newton; Nicholas G. Polson; Jianeng Xu

    We introduce and develop a weighted Bayesian bootstrap (WBB) for machine learning and statistics. WBB provides uncertainty quantification by sampling from a high dimensional posterior distribution. WBB is computationally fast and scalable using only off‐the‐shelf optimization software. First‐order asymptotic analysis provides a theoretical justification under suitable regularity conditions on the statistical

    更新日期:2020-08-20
  • Estimation of nonparametric additive models with high order spatial autoregressive errors
    Can. J. Stat. (IF 0.656) Pub Date : 2020-08-20
    Guoying Xu; Yang Bai

    In this article, we propose nonparametric generalized method of moments estimation for nonparametric additive models with high order spatial autoregressive dependence. The estimation procedure is derived in three steps by combining a spline‐backfitting method with generalized moment conditions that relieve correlations within the dependent variables. Consistency and asymptotic normality are demonstrated

    更新日期:2020-08-20
  • A Bayesian mixture of experts approach to covariate misclassification
    Can. J. Stat. (IF 0.656) Pub Date : 2020-08-12
    Michelle Xia; P. Richard Hahn; Paul Gustafson

    This article considers misclassification of categorical covariates in the context of regression analysis; if unaccounted for, such errors usually result in mis‐estimation of model parameters. With the presence of additional covariates, we exploit the fact that explicitly modelling non‐differential misclassification with respect to the response leads to a mixture regression representation. Under the

    更新日期:2020-08-12
  • Empirical and conditional likelihoods for two‐phase studies
    Can. J. Stat. (IF 0.656) Pub Date : 2020-08-03
    Menglu Che; Jerald F. Lawless; Peisong Han

    Two‐phase, response‐dependent sampling is often used in regression settings that involve expensive covariate measurements. Conditional maximum likelihood (CML) is an attractive approach in many cases as it avoids modelling of the covariate distribution, unlike full maximum likelihood. Scott & Wild (2011) introduced an augmented CML approach which is semi‐parametric efficient in certain settings with

    更新日期:2020-08-03
  • On variable ordination of Cholesky‐based estimation for a sparse covariance matrix
    Can. J. Stat. (IF 0.656) Pub Date : 2020-07-28
    Xiaoning Kang; Xinwei Deng

    Estimation of a large sparse covariance matrix is of great importance for statistical analysis, especially in high‐dimensional settings. The traditional approach such as the sample covariance matrix performs poorly due to the high dimensionality. The modified Cholesky decomposition (MCD) is a commonly used method for sparse covariance matrix estimation. However, the MCD method relies on the order of

    更新日期:2020-07-29
  • Regression modelling with the tilted beta distribution: A Bayesian approach
    Can. J. Stat. (IF 0.656) Pub Date : 2020-07-28
    Eugene D. Hahn

    Beta regression models are commonly used in the case of a dependent variable y that exists on the range (0,1). However, when y can additionally take on the values of zero and/or one, limitations of the beta distribution and beta regression models become apparent. One recent approach is to use an inflated beta regression model which has discrete point‐valued components. In this article, we introduce

    更新日期:2020-07-28
  • On‐line partitioning of the sample space in the regional adaptive algorithm
    Can. J. Stat. (IF 0.656) Pub Date : 2020-07-28
    Nicolas Grenon‐Godbout; Mylène Bédard

    The regional adaptive (RAPT) algorithm is particularly useful in sampling from multimodal distributions. We propose an adaptive partitioning of the sample space, to be used in conjunction with the RAPT sampler and its variants. The adaptive partitioning consists in defining a hyperplane that is orthogonal to the line joining averaged coordinates in two separate regions and that goes through a point

    更新日期:2020-07-28
  • Continuous threshold models with two‐way interactions in survival analysis
    Can. J. Stat. (IF 0.656) Pub Date : 2020-07-28
    Shuo Shuo Liu; Bingshu E. Chen

    Proportional hazards model with the biomarker–treatment interaction plays an important role in the survival analysis of the subset treatment effect. A threshold parameter for a continuous biomarker variable defines the subset of patients who can benefit or lose from a certain new treatment. In this article, we focus on a continuous threshold effect using the rectified linear unit and propose a gradient

    更新日期:2020-07-28
  • A sequential split‐and‐conquer approach for the analysis of big dependent data in computer experiments
    Can. J. Stat. (IF 0.656) Pub Date : 2020-07-28
    Chengrui Li; Ying Hung; Minge Xie

    Massive correlated data with many inputs are often generated from computer experiments to study complex systems. The Gaussian process (GP) model is a widely used tool for the analysis of computer experiments. Although GPs provide a simple and effective approximation to computer experiments, two critical issues remain unresolved. One is the computational issue in GP estimation and prediction where intensive

    更新日期:2020-07-28
  • Copula‐based predictions in small area estimation
    Can. J. Stat. (IF 0.656) Pub Date : 2020-07-07
    Kanika Grover; Elif F. Acar; Mahmoud Torabi

    Unit‐level regression models are commonly used in small area estimation (SAE) to obtain an empirical best linear unbiased prediction of small area characteristics. The underlying assumptions of these models, however, may be unrealistic in some applications. Previous work developed a copula‐based SAE model where the empirical Kendall's tau was used to estimate the dependence between two units from the

    更新日期:2020-07-07
  • Homogeneity testing under finite location‐scale mixtures
    Can. J. Stat. (IF 0.656) Pub Date : 2020-07-02
    Jiahua Chen; Pengfei Li; Guanfu Liu

    The testing problem for the order of finite mixture models has a long history and remains an active research topic. Since Ghosh & Sen (1985) revealed the hard‐to‐manage asymptotic properties of the likelihood ratio test, many successful alternative approaches have been developed. The most successful attempts include the modified likelihood ratio test and the EM‐test, which lead to neat solutions for

    更新日期:2020-07-02
  • Inference for misclassified multinomial data with covariates
    Can. J. Stat. (IF 0.656) Pub Date : 2020-06-16
    Shijia Wang; Liangliang Wang; Tim B. Swartz

    This article considers multinomial data subject to misclassification in the presence of covariates which affect both the misclassification probabilities and the true classification probabilities. A subset of the data may be subject to a secondary measurement according to an infallible classifier. Computations are carried out in a Bayesian setting where it is seen that the prior has an important role

    更新日期:2020-06-16
  • Correlated and misclassified binary observations in complex surveys
    Can. J. Stat. (IF 0.656) Pub Date : 2020-05-30
    Hon Yiu‐10 So; Mary E. Thompson; Changbao Wu

    Misclassifications in binary responses have long been a common problem in medical and health surveys. One way to handle misclassifications in clustered or longitudinal data is to incorporate the misclassification model through the generalized estimating equation (GEE) approach. However, existing methods are developed under a non‐survey setting and cannot be used directly for complex survey data. We

    更新日期:2020-05-30
  • Nonparametric beta kernel estimator for long and short memory time series
    Can. J. Stat. (IF 0.656) Pub Date : 2020-04-21
    Taoufik Bouezmarni, Sébastien Bellegem, Yassir Rabhi

    In this article we introduce a nonparametric estimator of the spectral density by smoothing the periodogram using beta kernel density. The estimator is proved to be bounded for short memory data and diverges at the origin for long memory data. The convergence in probability of the relative error and Monte Carlo simulations show that the proposed estimator automatically adapts to the long‐ and the short‐range

    更新日期:2020-04-21
  • Estimation in the Cox cure model with covariates missing not at random, with application to disease screening/prediction
    Can. J. Stat. (IF 0.656) Pub Date : 2020-04-17
    Lisha Guo; Yi Xiong; X. Joan Hu

    In an attempt to provide a statistical tool for disease screening and prediction, we propose a semiparametric approach to analysis of the Cox proportional hazards cure model in situations where the observations on the event time are subject to right censoring and some covariates are missing not at random. To facilitate the methodological development, we begin with semiparametric maximum likelihood

    更新日期:2020-04-17
  • Optimal balanced block designs for correlated observations
    Can. J. Stat. (IF 0.656) Pub Date : 2020-04-14
    Razieh Khodsiani, Saeid Pooladsaz

    The construction of universally optimal designs, if such exist, is difficult to obtain, especially when there are some nuisance effects or correlated errors. The hub correlation is a special correlation structure with applications to experiments in genetics, networks and other areas in industry and agriculture. There may be restrictions on the correlation values of the hub structure depending on the

    更新日期:2020-04-14
  • A semiparametric stochastic mixed effects model for bivariate cyclic longitudinal data
    Can. J. Stat. (IF 0.656) Pub Date : 2020-03-19
    Kexin Ji, Joel A. Dubin

    We propose a flexible semiparametric stochastic mixed effects model for bivariate cyclic longitudinal data. The model can handle either single cycle or, more generally, multiple consecutive cycle data. The approach models the mean of responses by parametric fixed effects and a smooth nonparametric function for the underlying time effects, and the relationship across the bivariate responses by a bivariate

    更新日期:2020-03-19
  • Partial deconvolution estimation in nonparametric regression
    Can. J. Stat. (IF 0.656) Pub Date : 2020-03-18
    Jianhong Shi, Xiuqin Bai, Weixing Song

    In this article, we propose a class of partial deconvolution kernel estimators for the nonparametric regression function when some covariates are measured with error and some are not. The estimation procedure combines the classical kernel methodology and the deconvolution kernel technique. According to whether the measurement error is ordinarily smooth or supersmooth, we establish the optimal local

    更新日期:2020-03-18
  • On the role of local blockchain network features in cryptocurrency price formation
    Can. J. Stat. (IF 0.656) Pub Date : 2020-03-18
    Asim K. Dey, Cuneyt G. Akcora, Yulia R. Gel, Murat Kantarcioglu

    Cryptocurrencies and the underpinning blockchain technology have gained unprecedented public attention recently. In contrast to fiat currencies, transactions of cryptocurrencies, such as Bitcoin and Litecoin, are permanently recorded on distributed ledgers to be seen by the public. As a result, public availability of all cryptocurrency transactions allows us to create a complex network of financial

    更新日期:2020-03-18
  • Estimation of the additive hazards model with interval‐censored data and missing covariates
    Can. J. Stat. (IF 0.656) Pub Date : 2020-03-18
    Huiqiong Li, Han Zhang, Liang Zhu, Ni Li, Jianguo Sun

    The additive hazards model is one of the most commonly used regression models in the analysis of failure time data and many methods have been developed for its inference in various situations. However, no established estimation procedure exists when there are covariates with missing values and the observed responses are interval‐censored; both types of complications arise in various settings including

    更新日期:2020-03-18
  • Nonparametric change point detection for periodic time series
    Can. J. Stat. (IF 0.656) Pub Date : 2020-03-11
    Lingzhe Guo, Reza Modarres

    We consider detection of multiple changes in the distribution of periodic and autocorrelated data with known period. To account for periodicity we transform the sequence of vector observations by arranging them in matrices and thereby producing a sequence of independently and identically distributed matrix observations. We propose methods of testing the equality of matrix distributions and present

    更新日期:2020-03-11
  • Post model‐fitting exploration via a “Next‐Door” analysis
    Can. J. Stat. (IF 0.656) Pub Date : 2020-03-05
    Leying Guan, Robert Tibshirani

    We propose a simple method for evaluating the model that has been chosen by an adaptive regression procedure, our main focus being the lasso. This procedure deletes each chosen predictor and refits the lasso to get a set of models that are “close” to the chosen “base model,” and compares the error rates of the base model with that of nearby models. If the deletion of a predictor leads to significant

    更新日期:2020-03-05
  • Robust multivariate change point analysis based on data depth
    Can. J. Stat. (IF 0.656) Pub Date : 2020-03-05
    Shojaeddin Chenouri, Ahmad Mozaffari, Gregory Rice

    Modern methods for detecting changes in the scale or covariance of multivariate distributions rely primarily on testing for the constancy of the covariance matrix. These depend on higher‐order moment conditions, and also do not work well when the dimension of the data is large or even moderate relative to the sample size. In this paper, we propose a nonparametric change point test for multivariate

    更新日期:2020-03-05
  • Empirical likelihood for nonlinear regression models with nonignorable missing responses
    Can. J. Stat. (IF 0.656) Pub Date : 2020-03-02
    Zhihuang Yang, Niansheng Tang

    This article develops three empirical likelihood (EL) approaches to estimate parameters in nonlinear regression models in the presence of nonignorable missing responses. These are based on the inverse probability weighted (IPW) method, the augmented IPW (AIPW) method and the imputation technique. A logistic regression model is adopted to specify the propensity score. Maximum likelihood estimation is

    更新日期:2020-03-02
  • The entropic measure transform
    Can. J. Stat. (IF 0.656) Pub Date : 2020-02-11
    Renjie Wang, Cody Hyndman, Anastasis Kratsios

    We introduce the entropic measure transform (EMT) problem for a general process and prove the existence of a unique optimal measure characterizing the solution. The density process of the optimal measure is characterized using a semimartingale BSDE under general conditions. The EMT is used to reinterpret the conditional entropic risk‐measure and to obtain a convenient formula for the conditional expectation

    更新日期:2020-02-11
  • A backward procedure for change‐point detection with applications to copy number variation detection
    Can. J. Stat. (IF 0.656) Pub Date : 2020-02-05
    Seung Jun Shin, Yichao Wu, Ning Hao

    Change‐point detection regains much attention recently for analyzing array or sequencing data for copy number variation (CNV) detection. In such applications, the true signals are typically very short and buried in the long data sequence, which makes it challenging to identify the variations efficiently and accurately. In this article, we propose a new change‐point detection method, a backward procedure

    更新日期:2020-02-05
  • Common‐factor stochastic volatility modelling with observable proxy
    Can. J. Stat. (IF 0.656) Pub Date : 2020-01-29
    Yizhou Fang, Martin Lysy, Don Mcleish

    Multi‐asset modelling is of fundamental importance to financial applications such as risk management and portfolio selection. In this article, we propose a multivariate stochastic volatility modelling framework with a parsimonious and interpretable correlation structure. Building on well‐established evidence of common volatility factors among individual assets, we consider a multivariate diffusion

    更新日期:2020-01-29
  • Goodness‐of‐fit for regime‐switching copula models with application to option pricing
    Can. J. Stat. (IF 0.656) Pub Date : 2020-01-29
    Bouchra R. Nasri, Bruno N. Rémillard, Mamadou Y. Thioub

    We consider several time series, and for each of them, we fit an appropriate dynamic parametric model. This produces serially independent error terms for each time series. The dependence between these error terms is then modelled by a regime‐switching copula. The EM algorithm is used for estimating the parameters and a sequential goodness‐of‐fit procedure based on Cramér–von Mises statistics is proposed

    更新日期:2020-01-29
  • Functional measurement error in functional regression
    Can. J. Stat. (IF 0.656) Pub Date : 2020-01-05
    Sneha Jadhav, Shuangge Ma

    Measurement error is an important problem that has not been studied very well in the context of functional data analysis. To the best of our knowledge, there are no existing methods that address the presence of functional measurement errors in generalized functional linear models. In this article, a novel approach is proposed to estimate the slope function in the presence of measurement error in the

    更新日期:2020-01-05
  • Improved methods for moment restriction models with data combination and an application to two‐sample instrumental variable estimation
    Can. J. Stat. (IF 0.656) Pub Date : 2019-12-26
    Heng Shu, Zhiqiang Tan

    Combining‐100 information from multiple samples is often needed in biomedical and economic studies, but differences between these samples must be appropriately taken into account in the analysis of the combined data. We study the estimation for moment restriction models with data combined from two samples under an ignorability‐type assumption while allowing for different marginal distributions of variables

    更新日期:2019-12-26
  • Optimal design for classification of functional data
    Can. J. Stat. (IF 0.656) Pub Date : 2019-12-19
    Cai Li, Luo Xiao

    We study the design problem for the optimal classification of functional data. The goal is to select sampling time points so that functional data observed at these time points can be classified accurately. We propose optimal designs that are applicable to either dense or sparse functional data. Using linear discriminant analysis, we formulate our design objectives as explicit functions of the sampling

    更新日期:2019-12-19
  • High‐dimensional covariance matrix estimation using a low‐rank and diagonal decomposition
    Can. J. Stat. (IF 0.656) Pub Date : 2019-12-19
    Yilei Wu, Yingli Qin, Mu Zhu

    We study high‐dimensional covariance/precision matrix estimation under the assumption that the covariance/precision matrix can be decomposed into a low‐rank component L and a diagonal component D . The rank of L can either be chosen to be small or controlled by a penalty function. Under moderate conditions on the population covariance/precision matrix itself and on the penalty function, we prove some

    更新日期:2019-12-19
  • Semiparametric regression methods for temporal processes subject to multiple sources of censoring
    Can. J. Stat. (IF 0.656) Pub Date : 2019-12-18
    Tianyu Zhan, Douglas E. Schaubel

    Process regression methodology is underdeveloped relative to the frequency with which pertinent data arise. In this article, the response‐190 is a binary indicator process representing the joint event of being alive and remaining in a specific state. The process is indexed by time (e.g., time since diagnosis) and observed continuously. Data of this sort occur frequently in the study of chronic disease

    更新日期:2019-12-18
  • Using ranked set sampling with binary outcomes in cluster randomized designs
    Can. J. Stat. (IF 0.656) Pub Date : 2019-12-18
    Xinlei Wang, Mumu Wang, Johan Lim, Soohyun Ahn

    We study the use of ranked set sampling (RSS) with binary outcomes in cluster‐randomized designs (CRDs), where a generalized linear mixed model (GLMM) is used to model the hierarchical data structure involved. Under the GLMM‐based framework, we propose three different approaches to estimate the treatment effect, including the nonparametric (NP), maximum likelihood (ML) and pseudo likelihood (PL) estimators

    更新日期:2019-12-18
  • Direct estimation of differential networks under high‐dimensional nonparanormal graphical models
    Can. J. Stat. (IF 0.656) Pub Date : 2019-12-12
    Qingyang Zhang

    In genomics, it is often of interest to study the structural change of a genetic network between two phenotypes. Under Gaussian graphical models, the problem can be transformed to estimating the difference between two precision matrices, and several approaches have been recently developed for this task such as joint graphical lasso and fused graphical lasso. However, the multivariate Gaussian assumptions

    更新日期:2019-12-12
  • Estimating prediction error for complex samples
    Can. J. Stat. (IF 0.656) Pub Date : 2019-12-11
    Andrew Holbrook, Thomas Lumley, Daniel Gillen

    With a growing interest in using non‐representative samples to train prediction models for numerous outcomes it is necessary to account for the sampling design that gives rise to the data in order to assess the generalized predictive utility of a proposed prediction rule. After learning a prediction rule based on a non‐uniform sample, it is of interest to estimate the rule's error rate when applied

    更新日期:2019-12-11
  • A new distribution‐free k‐sample test: Analysis of kernel density functionals
    Can. J. Stat. (IF 0.656) Pub Date : 2019-11-26
    Su Chen

    A novel distribution‐free k‐sample test of differences in location shifts based on the analysis of kernel density functional estimation is introduced and studied. The proposed test parallels one‐way analysis of variance and the Kruskal–Wallis (KW) test aiming at testing locations of unknown distributions. In contrast to the rank (score)‐transformed non‐parametric approach, such as the KW test, the

    更新日期:2019-11-26
  • Partial order relations for classification comparisons
    Can. J. Stat. (IF 0.656) Pub Date : 2019-11-20
    Lo‐Bin Chang

    The Bayes classification rule offers the optimal classifier, minimizing the classification error rate, whereas the Neyman–Pearson lemma offers the optimal family of classifiers to maximize the detection rate for any given false alarm rate. These motivate studies on comparing classifiers based on similarities between the classifiers and the optimal. In this article, we define partial order relations

    更新日期:2019-11-20
  • Validity and efficiency in analyzing ordinal responses with missing observations
    Can. J. Stat. (IF 0.656) Pub Date : 2019-10-17
    Xichen She, Changbao Wu

    This article addresses issues in creating public‐use data files in the presence of missing ordinal responses and subsequent statistical analyses of the dataset by users. The authors propose a fully efficient fractional imputation (FI) procedure for ordinal responses with missing observations. The proposed imputation strategy retrieves the missing values through the full conditional distribution of

    更新日期:2019-10-17
  • Doubly sparse regression incorporating graphical structure among predictors
    Can. J. Stat. (IF 0.656) Pub Date : 2019-09-13
    Matthew Stephenson, R. Ayesha Ali, Gerarda A. Darlington,

    Recent research has demonstrated that information learned from building a graphical model on the predictor set of a regularized linear regression model can be leveraged to improve prediction of a continuous outcome. In this article, we present a new model that encourages sparsity at both the level of the regression coefficients and the level of individual contributions in a decomposed representation

    更新日期:2019-09-13
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