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  • 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
  • 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:2020-07-29
  • 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
  • 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
  • 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:2020-07-08
  • 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:2020-07-03
  • 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 introduces 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

    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
  • 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
  • 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 non‐parametric (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
  • A consistent estimator for logistic mixed effect models.
    Can. J. Stat. (IF 0.656) Pub Date : 2019-07-06
    Yizheng Wei,Yanyuan Ma,Tanya P Garcia,Samiran Sinha

    We propose a consistent and locally efficient estimator to estimate the model parameters for a logistic mixed effect model with random slopes. Our approach relaxes two typical assumptions: the random effects being normally distributed, and the covariates and random effects being independent of each other. Adhering to these assumptions is particularly difficult in health studies where in many cases

    更新日期:2019-11-01
  • A Review of Statistical Methods in Imaging Genetics.
    Can. J. Stat. (IF 0.656) Pub Date : 2019-02-25
    Farouk S Nathoo,Linglong Kong,Hongtu Zhu

    With the rapid growth of modern technology, many biomedical studies are being conducted to collect massive datasets with volumes of multi-modality imaging, genetic, neurocognitive, and clinical information from increasingly large cohorts. Simultaneously extracting and integrating rich and diverse heterogeneous information in neuroimaging and/or genomics from these big datasets could transform our understanding

    更新日期:2019-11-01
  • Locally optimal designs for some binary dose-response models.
    Can. J. Stat. (IF 0.656) Pub Date : 2018-10-06
    Yi Zhai,Zhide Fang

    In this paper, we consider the problem of seeking locally optimal designs for nonlinear dose-response models with binary outcomes. Applying the theory of Tchebycheff Systems and other algebraic tools, we show that the locally D-, A-, and c-optimal designs for three binary dose-response models are minimally supported in finite, closed design intervals. The methods to obtain such designs are presented

    更新日期:2019-11-01
  • Post-Selection Inference for ℓ1-Penalized Likelihood Models.
    Can. J. Stat. (IF 0.656) Pub Date : 2018-08-22
    Jonathan Taylor,Robert Tibshirani

    We present a new method for post-selection inference for ℓ1 (lasso)-penalized likelihood models, including generalized regression models. Our approach generalizes the post-selection framework presented in Lee et al. (2013). The method provides p-values and confidence intervals that are asymptotically valid, conditional on the inherent selection done by the lasso. We present applications of this work

    更新日期:2019-11-01
  • Online updating method with new variables for big data streams.
    Can. J. Stat. (IF 0.656) Pub Date : 2018-04-18
    Chun Wang,Ming-Hui Chen,Jing Wu,Jun Yan,Yuping Zhang,Elizabeth Schifano

    For big data arriving in streams, online updating is an important statistical method that breaks the storage barrier and the computational barrier under certain circumstances. In the regression context, online updating algorithms assume that the set of predictor variables does not change, and consequently cannot incorporate new variables that may become available midway through the data stream. A naive

    更新日期:2019-11-01
  • A new method for robust mixture regression.
    Can. J. Stat. (IF 0.656) Pub Date : 2017-06-06
    Chun Yu,Weixin Yao,Kun Chen

    Finite mixture regression models have been widely used for modelling mixed regression relationships arising from a clustered and thus heterogenous population. The classical normal mixture model, despite its simplicity and wide applicability, may fail in the presence of severe outliers. Using a sparse, case-specific, and scale-dependent mean-shift mixture model parameterization, we propose a robust

    更新日期:2019-11-01
  • Estimating treatment effects in observational studies with both prevalent and incident cohorts.
    Can. J. Stat. (IF 0.656) Pub Date : 2017-04-13
    Jing Ning,Chuan Hong,Liang Li,Xuelin Huang,Yu Shen

    Registry databases are increasingly being used for comparative effectiveness research in cancer. Such databases reflect the real-world patient population and physician practice, and thus are natural sources for comparing multiple treatment scenarios and the associated long-term clinical outcomes. Registry databases usually include both incident and prevalent cohorts, which provide valuable complementary

    更新日期:2019-11-01
  • Probability-scale residuals for continuous, discrete, and censored data.
    Can. J. Stat. (IF 0.656) Pub Date : 2017-03-30
    Bryan E Shepherd,Chun Li,Qi Liu

    We describe a new residual for general regression models, defined as pr(Y* < y) - pr(Y* > y), where y is the observed outcome and Y* is a random variable from the fitted distribution. This probability-scale residual can be written as E {sign(y, Y*)} whereas the popular observed-minus-expected residual can be thought of as E(y - Y*). Therefore, the probability-scale residual is useful in settings where

    更新日期:2019-11-01
  • A semivarying joint model for longitudinal binary and continuous outcomes.
    Can. J. Stat. (IF 0.656) Pub Date : 2016-09-27
    Esra Kürüm,John Hughes,Runze Li

    Semivarying models extend varying coefficient models by allowing some regression coefficients to be constant with respect to the underlying covariate(s). In this paper we develop a semivarying joint modelling framework for estimating the time-varying association between two intensively measured longitudinal response: a continuous one and a binary one. To overcome the major challenge of jointly modelling

    更新日期:2019-11-01
  • Variable Selection and Inference Procedures for Marginal Analysis of Longitudinal Data with Missing Observations and Covariate Measurement Error.
    Can. J. Stat. (IF 0.656) Pub Date : 2016-02-16
    Grace Y Yi,Xianming Tan,Runze Li

    In contrast to extensive attention on model selection for univariate data, research on model selection for longitudinal data remains largely unexplored. This is particularly the case when data are subject to missingness and measurement error. To address this important problem, we propose marginal methods that simultaneously carry out model selection and estimation for longitudinal data with missing

    更新日期:2019-11-01
  • Statistical inference for the additive hazards model under outcome-dependent sampling.
    Can. J. Stat. (IF 0.656) Pub Date : 2015-09-18
    Jichang Yu,Yanyan Liu,Dale P Sandler,Haibo Zhou

    Cost-effective study design and proper inference procedures for data from such designs are always of particular interests to study investigators. In this article, we propose a biased sampling scheme, an outcome-dependent sampling (ODS) design for survival data with right censoring under the additive hazards model. We develop a weighted pseudo-score estimator for the regression parameters for the proposed

    更新日期:2019-11-01
  • Stochastic dynamic models and Chebyshev splines.
    Can. J. Stat. (IF 0.656) Pub Date : 2015-06-06
    Ruzong Fan,Bin Zhu,Yuedong Wang

    In this article, we establish a connection between a stochastic dynamic model (SDM) driven by a linear stochastic differential equation (SDE) and a Chebyshev spline, which enables researchers to borrow strength across fields both theoretically and numerically. We construct a differential operator for the penalty function and develop a reproducing kernel Hilbert space (RKHS) induced by the SDM and the

    更新日期:2019-11-01
  • A semiparametric linear transformation model to estimate causal effects for survival data.
    Can. J. Stat. (IF 0.656) Pub Date : 2014-03-01
    Huazhen Lin,Yi Li,Liang Jiang,Gang Li

    Semiparametric linear transformation models serve as useful alternatives to the Cox proportional hazard model. In this study, we use the semiparametric linear transformation model to analyze survival data with selective compliance. We estimate regression parameters and the transformation function based on pseudo-likelihood and a series of estimating equations. We show that the estimators for the regression

    更新日期:2019-11-01
  • Estimation with Right-Censored Observations Under A Semi-Markov Model.
    Can. J. Stat. (IF 0.656) Pub Date : 2013-07-23
    Lihui Zhao,X Joan Hu

    The semi-Markov process often provides a better framework than the classical Markov process for the analysis of events with multiple states. The purpose of this paper is twofold. First, we show that in the presence of right censoring, when the right end-point of the support of the censoring time is strictly less than the right end-point of the support of the semi-Markov kernel, the transition probability

    更新日期:2019-11-01
  • Parallelism, uniqueness, and large-sample asymptotics for the Dantzig selector.
    Can. J. Stat. (IF 0.656) Pub Date : 2013-03-01
    Lee Dicker,Xihong Lin

    The Dantzig selector (Candès and Tao, 2007) is a popular ℓ1-regularization method for variable selection and estimation in linear regression. We present a very weak geometric condition on the observed predictors which is related to parallelism and, when satisfied, ensures the uniqueness of Dantzig selector estimators. The condition holds with probability 1, if the predictors are drawn from a continuous

    更新日期:2019-11-01
  • Q-learning for estimating optimal dynamic treatment rules from observational data.
    Can. J. Stat. (IF 0.656) Pub Date : 2013-01-29
    Erica E M Moodie,Bibhas Chakraborty,Michael S Kramer

    The area of dynamic treatment regimes (DTR) aims to make inference about adaptive, multistage decision-making in clinical practice. A DTR is a set of decision rules, one per interval of treatment, where each decision is a function of treatment and covariate history that returns a recommended treatment. Q-learning is a popular method from the reinforcement learning literature that has recently been

    更新日期:2019-11-01
  • Variable selection and estimation in generalized linear models with the seamless L0 penalty.
    Can. J. Stat. (IF 0.656) Pub Date : 2012-12-01
    Zilin Li,Sijian Wang,Xihong Lin

    In this paper, we propose variable selection and estimation in generalized linear models using the seamless L0 (SELO) penalized likelihood approach. The SELO penalty is a smooth function that very closely resembles the discontinuous L0 penalty. We develop an e cient algorithm to fit the model, and show that the SELO-GLM procedure has the oracle property in the presence of a diverging number of variables

    更新日期:2019-11-01
  • Semiparametric transformation models for multivariate panel count data with dependent observation process.
    Can. J. Stat. (IF 0.656) Pub Date : 2012-06-12
    Ni Li,Do-Hwan Park,Jianguo Sun,Kyungmann Kim

    This article discusses regression analysis of multivariate panel count data in which the observation process may contain relevant information about or be related to the underlying recurrent event processes of interest. Such data occur if a recurrent event study involves several related types of recurrent events and the observation scheme or process may be subject-specific. For the problem, a class

    更新日期:2019-11-01
  • Robust penalized logistic regression with truncated loss functions.
    Can. J. Stat. (IF 0.656) Pub Date : 2011-12-14
    Seo Young Park,Yufeng Liu

    The penalized logistic regression (PLR) is a powerful statistical tool for classification. It has been commonly used in many practical problems. Despite its success, since the loss function of the PLR is unbounded, resulting classifiers can be sensitive to outliers. To build more robust classifiers, we propose the robust PLR (RPLR) which uses truncated logistic loss functions, and suggest three schemes

    更新日期:2019-11-01
  • Measurement error modeling and nutritional epidemiology association analyses.
    Can. J. Stat. (IF 0.656) Pub Date : 2011-09-01
    Ross L Prentice,Ying Huang

    This paper summarizes the results of a Nutrient Biomarker Study in the Women's Health Initiative, and its application to studies of the association between energy and protein consumption and the risk of major cancers and cardiovascular diseases. The presentation emphasizes measurement error modeling and related data analysis methods, since addressing measurement issues appears to be central to these

    更新日期:2019-11-01
  • The effect of misspecification of random effects distributions in clustered data settings with outcome-dependent sampling.
    Can. J. Stat. (IF 0.656) Pub Date : 2011-09-01
    John M Neuhaus,Charles E McCulloch

    Genetic epidemiologists often gather outcome-dependent samples of family data to measure within-family associations of genetic factors with disease outcomes. Generalized linear mixed models provide effective methods to estimate within-family associations but typically require parametric specification of the random effects distribution. Although misspecification of the random effects distribution often

    更新日期:2019-11-01
  • Additive hazards regression with censoring indicators missing at random.
    Can. J. Stat. (IF 0.656) Pub Date : 2011-01-05
    Xinyuan Song,Liuquan Sun,Xiaoyun Mu,Gregg E Dinse

    In this article, the authors consider a semiparametric additive hazards regression model for right-censored data that allows some censoring indicators to be missing at random. They develop a class of estimating equations and use an inverse probability weighted approach to estimate the regression parameters. Nonparametric smoothing techniques are employed to estimate the probability of non-missingness

    更新日期:2019-11-01
  • Longitudinal functional principal component modeling via Stochastic Approximation Monte Carlo.
    Can. J. Stat. (IF 0.656) Pub Date : 2010-08-07
    Josue G Martinez,Faming Liang,Lan Zhou,Raymond J Carroll

    The authors consider the analysis of hierarchical longitudinal functional data based upon a functional principal components approach. In contrast to standard frequentist approaches to selecting the number of principal components, the authors do model averaging using a Bayesian formulation. A relatively straightforward reversible jump Markov Chain Monte Carlo formulation has poor mixing properties and

    更新日期:2019-11-01
  • Inference after variable selection using restricted permutation methods.
    Can. J. Stat. (IF 0.656) Pub Date : 2010-04-07
    Rui Wang,Stephen W Lagakos

    When confronted with multiple covariates and a response variable, analysts sometimes apply a variable-selection algorithm to the covariate-response data to identify a subset of covariates potentially associated with the response, and then wish to make inferences about parameters in a model for the marginal association between the selected covariates and the response. If an independent data set were

    更新日期:2019-11-01
  • Some aspects of modern population mathematics.
    Can. J. Stat. (IF 0.656) Pub Date : 1981-01-01
    D R Brillinger

    "The purpose of this paper is to survey a number of the technical tools and models that have found use in the study of human and other populations, and to indicate some problems of current interest. These tools and models are varied: integral equations, nonlinear oscillations, differential geometry, dynamical systems, nonlinear operation, bifurcation theory, semigroup theory, martingale theory, Markov

    更新日期:2019-11-01
  • 更新日期:2019-11-01
  • 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
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