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Censored autoregressive regression models with Student‐t innovations Can. J. Stat. (IF 0.6) Pub Date : 2024-02-21 Katherine A. L. Valeriano, Fernanda L. Schumacher, Christian E. Galarza, Larissa A. Matos
Data collected over time are common in applications and may contain censored or missing observations, making it difficult to use standard statistical procedures. This article proposes an algorithm to estimate the parameters of a censored linear regression model with errors serially correlated and innovations following a Student‐ distribution. This distribution is widely used in the statistical modelling
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Semiparametric estimation for the functional additive hazards model Can. J. Stat. (IF 0.6) Pub Date : 2024-01-11 Meiling Hao, Kin-yat Liu, Wen Su, Xingqiu Zhao
We propose a new functional additive hazards model to investigate the potential effects of functional and scalar predictors on mortality risks, and develop a penalized least squares estimation method for model parameters based on a pseudoscore estimating equation. A reproducing kernel Hilbert space approach is used to establish the consistency, convergence rate, and joint asymptotic distribution of
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Clustering spatial functional data using a geographically weighted Dirichlet process Can. J. Stat. (IF 0.6) Pub Date : 2024-01-05 Tianyu Pan, Weining Shen, Guanyu Hu
We propose a Bayesian nonparametric clustering approach to study the spatial heterogeneity effect for functional data observed at spatially correlated locations. We consider a geographically weighted Chinese restaurant process equipped with a conditional autoregressive prior to capture fully the spatial correlation of function curves. To sample efficiently from our model, we customize a prior called
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Bayesian Model Selection via Composite Likelihood for High-dimensional Data Integration Can. J. Stat. (IF 0.6) Pub Date : 2024-01-05 Guanlin Zhang, Yuehua Wu, Xin Gao
We consider data integration problems where correlated data are collected from multiple platforms. Within each platform, there are linear relationships between the responses and a collection of predictors. We extend the linear models to include random errors coming from a much wider family of sub-Gaussian and subexponential distributions. The goal is to select important predictors across multiple platforms
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Modelling occurrence and quantity of longitudinal semicontinuous data simultaneously with nonparametric unobserved heterogeneity Can. J. Stat. (IF 0.6) Pub Date : 2023-12-09 Guohua Yan, Renjun Ma
Semicontinuous data frequently occur in longitudinal studies. The popular two-part modelling approach deals with longitudinal semicontinuous data by analyzing the occurrence of positive values and the intensity of positive values separately; however, this separation may break down the natural sequence of semicontinuous data within a subject and destroy its serial dependence structure. In this article
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Efficient multiply robust imputation in the presence of influential units in surveys Can. J. Stat. (IF 0.6) Pub Date : 2023-11-22 Sixia Chen, David Haziza, Victoire Michal
Item nonresponse is a common issue in surveys. Because unadjusted estimators may be biased in the presence of nonresponse, it is common practice to impute the missing values with the objective of reducing the nonresponse bias as much as possible. However, commonly used imputation procedures may lead to unstable estimators of population totals/means when influential units are present in the set of respondents
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Efficient multiply robust imputation in the presence of influential units in surveys Can. J. Stat. (IF 0.6) Pub Date : 2023-11-22 Sixia Chen, David Haziza, Victoire Michal
Item nonresponse is a common issue in surveys. Because unadjusted estimators may be biased in the presence of nonresponse, it is common practice to impute the missing values with the objective of reducing the nonresponse bias as much as possible. However, commonly used imputation procedures may lead to unstable estimators of population totals/means when influential units are present in the set of respondents
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Football group draw probabilities and corrections Can. J. Stat. (IF 0.6) Pub Date : 2023-11-10 Gareth O. Roberts, Jeffrey S. Rosenthal
This article considers the challenge of designing football group draw mechanisms, which have a uniform distribution over all valid draw assignments, but are also entertaining, practical and transparent. Although this problem is trivial in completely symmetric problems, it becomes challenging when there are draw constraints that are not exchangeable across each of the competing teams, so that symmetry
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Nonparametric estimation of a survival function in the presence of measurement errors on the failure time of interest Can. J. Stat. (IF 0.6) Pub Date : 2023-11-10 Shaojia Jin, Yanyan Liu, Guangcai Mao, Jianguo Sun, Yuanshan Wu
This article discusses nonparametric estimation of a survival function in the presence of measurement errors on the observation of the failure time of interest. One situation where such issues arise would be clinical studies of chronic diseases where the observation on the time to the failure event of interest such as the onset of the disease relies on patient recall or chart review of electronic medical
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Football group draw probabilities and corrections Can. J. Stat. (IF 0.6) Pub Date : 2023-11-10 Gareth O. Roberts, Jeffrey S. Rosenthal
This article considers the challenge of designing football group draw mechanisms, which have a uniform distribution over all valid draw assignments, but are also entertaining, practical and transparent. Although this problem is trivial in completely symmetric problems, it becomes challenging when there are draw constraints that are not exchangeable across each of the competing teams, so that symmetry
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Nonparametric estimation of a survival function in the presence of measurement errors on the failure time of interest Can. J. Stat. (IF 0.6) Pub Date : 2023-11-10 Shaojia Jin, Yanyan Liu, Guangcai Mao, Jianguo Sun, Yuanshan Wu
This article discusses nonparametric estimation of a survival function in the presence of measurement errors on the observation of the failure time of interest. One situation where such issues arise would be clinical studies of chronic diseases where the observation on the time to the failure event of interest such as the onset of the disease relies on patient recall or chart review of electronic medical
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Fused mean structure learning in data integration with dependence Can. J. Stat. (IF 0.6) Pub Date : 2023-10-27 Emily C. Hector
Motivated by image-on-scalar regression with data aggregated across multiple sites, we consider a setting in which multiple independent studies each collect multiple dependent vector outcomes, with potential mean model parameter homogeneity between studies and outcome vectors. To determine the validity of a joint analysis of these data sources, we must learn which of them share mean model parameters
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Fused mean structure learning in data integration with dependence Can. J. Stat. (IF 0.6) Pub Date : 2023-10-27 Emily C. Hector
Motivated by image-on-scalar regression with data aggregated across multiple sites, we consider a setting in which multiple independent studies each collect multiple dependent vector outcomes, with potential mean model parameter homogeneity between studies and outcome vectors. To determine the validity of a joint analysis of these data sources, we must learn which of them share mean model parameters
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Contrast tests for groups of functional data Can. J. Stat. (IF 0.6) Pub Date : 2023-08-19 Quyen Do, Pang Du
Functional analysis of variance (ANOVA) models are often used to compare groups of functional data. Similar to the traditional ANOVA model, a common follow-up procedure to the rejection of the functional ANOVA null hypothesis is to perform functional linear contrast tests to identify which groups have different mean functions. Most existing functional contrast tests assume independent functional observations
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Robust joint modelling of sparsely observed paired functional data Can. J. Stat. (IF 0.6) Pub Date : 2023-08-19 Huiya Zhou, Xiaomeng Yan, Lan Zhou
A reduced-rank mixed-effects model is developed for robust modelling of sparsely observed paired functional data. In this model, the curves for each functional variable are summarized using a few functional principal components, and the association of the two functional variables is modelled through the association of the principal component scores. A multivariate-scale mixture of normal distributions
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High-dimensional variable selection accounting for heterogeneity in regression coefficients across multiple data sources Can. J. Stat. (IF 0.6) Pub Date : 2023-08-19 Tingting Yu, Shangyuan Ye, Rui Wang
When analyzing data combined from multiple sources (e.g., hospitals, studies), the heterogeneity across different sources must be accounted for. In this article, we consider high-dimensional linear regression models for integrative data analysis. We propose a new adaptive clustering penalty (ACP) method to simultaneously select variables and cluster source-specific regression coefficients with subhomogeneity
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Contrast tests for groups of functional data Can. J. Stat. (IF 0.6) Pub Date : 2023-08-19 Quyen Do, Pang Du
Functional analysis of variance (ANOVA) models are often used to compare groups of functional data. Similar to the traditional ANOVA model, a common follow-up procedure to the rejection of the functional ANOVA null hypothesis is to perform functional linear contrast tests to identify which groups have different mean functions. Most existing functional contrast tests assume independent functional observations
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Robust joint modelling of sparsely observed paired functional data Can. J. Stat. (IF 0.6) Pub Date : 2023-08-19 Huiya Zhou, Xiaomeng Yan, Lan Zhou
A reduced-rank mixed-effects model is developed for robust modelling of sparsely observed paired functional data. In this model, the curves for each functional variable are summarized using a few functional principal components, and the association of the two functional variables is modelled through the association of the principal component scores. A multivariate-scale mixture of normal distributions
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High-dimensional variable selection accounting for heterogeneity in regression coefficients across multiple data sources Can. J. Stat. (IF 0.6) Pub Date : 2023-08-19 Tingting Yu, Shangyuan Ye, Rui Wang
When analyzing data combined from multiple sources (e.g., hospitals, studies), the heterogeneity across different sources must be accounted for. In this article, we consider high-dimensional linear regression models for integrative data analysis. We propose a new adaptive clustering penalty (ACP) method to simultaneously select variables and cluster source-specific regression coefficients with subhomogeneity
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Clustering and semi-supervised classification for clickstream data via mixture models Can. J. Stat. (IF 0.6) Pub Date : 2023-08-17 Michael P. B. Gallaugher, Paul D. McNicholas
Finite mixture models have been used for unsupervised learning for some time, and their use within the semisupervised paradigm is becoming more commonplace. Clickstream data are one of the various emerging data types that demand particular attention because there is a notable paucity of statistical learning approaches currently available. A mixture of first-order continuous-time Markov models is introduced
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Clustering and semi-supervised classification for clickstream data via mixture models Can. J. Stat. (IF 0.6) Pub Date : 2023-08-17 Michael P. B. Gallaugher, Paul D. McNicholas
Finite mixture models have been used for unsupervised learning for some time, and their use within the semisupervised paradigm is becoming more commonplace. Clickstream data are one of the various emerging data types that demand particular attention because there is a notable paucity of statistical learning approaches currently available. A mixture of first-order continuous-time Markov models is introduced
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High-dimensional model averaging for quantile regression Can. J. Stat. (IF 0.6) Pub Date : 2023-08-08 Jinhan Xie, Xianwen Ding, Bei Jiang, Xiaodong Yan, Linglong Kong
This article considers robust prediction issues in ultrahigh-dimensional (UHD) datasets and proposes combining quantile regression with sequential model averaging to arrive at a quantile sequential model averaging (QSMA) procedure. The QSMA method is made computationally feasible by employing a sequential screening process and a Bayesian information criterion (BIC) model averaging method for UHD quantile
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Identifiability constraints in generalized additive models Can. J. Stat. (IF 0.6) Pub Date : 2023-08-08 Alex Stringer
Identifiability constraints are necessary for parameter estimation when fitting models with nonlinear covariate associations. The choice of constraint affects standard errors of the estimated curve. Centring constraints are often applied by default because they are thought to yield lowest standard errors out of any constraint, but this claim has not been investigated. We show that whether centring
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High-dimensional model averaging for quantile regression Can. J. Stat. (IF 0.6) Pub Date : 2023-08-08 Jinhan Xie, Xianwen Ding, Bei Jiang, Xiaodong Yan, Linglong Kong
This article considers robust prediction issues in ultrahigh-dimensional (UHD) datasets and proposes combining quantile regression with sequential model averaging to arrive at a quantile sequential model averaging (QSMA) procedure. The QSMA method is made computationally feasible by employing a sequential screening process and a Bayesian information criterion (BIC) model averaging method for UHD quantile
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Identifiability constraints in generalized additive models Can. J. Stat. (IF 0.6) Pub Date : 2023-08-08 Alex Stringer
Identifiability constraints are necessary for parameter estimation when fitting models with nonlinear covariate associations. The choice of constraint affects standard errors of the estimated curve. Centring constraints are often applied by default because they are thought to yield lowest standard errors out of any constraint, but this claim has not been investigated. We show that whether centring
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Improved inference for a boundary parameter Can. J. Stat. (IF 0.6) Pub Date : 2023-08-04 Soumaya Elkantassi, Ruggero Bellio, Alessandra R. Brazzale, Anthony C. Davison
The limiting distributions of statistics used to test hypotheses about parameters on the boundary of their domains may provide very poor approximations to the finite-sample behaviour of these statistics, even for very large samples. We review theoretical work on this problem, describe hard and soft boundaries and iceberg estimators, and give examples highlighting how the limiting results greatly underestimate
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Improved inference for a boundary parameter Can. J. Stat. (IF 0.6) Pub Date : 2023-08-04 Soumaya Elkantassi, Ruggero Bellio, Alessandra R. Brazzale, Anthony C. Davison
The limiting distributions of statistics used to test hypotheses about parameters on the boundary of their domains may provide very poor approximations to the finite-sample behaviour of these statistics, even for very large samples. We review theoretical work on this problem, describe hard and soft boundaries and iceberg estimators, and give examples highlighting how the limiting results greatly underestimate
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Joint modelling of quantile regression for longitudinal data with information observation times and a terminal event Can. J. Stat. (IF 0.6) Pub Date : 2023-07-31 Weicai Pang, Yutao Liu, Xingqiu Zhao, Yong Zhou
Longitudinal data arise frequently in biomedical follow-up observation studies. Conditional mean regression and conditional quantile regression are two popular approaches to model longitudinal data. Many results are derived under the case where the response variables are independent of the observation times. In this article, we propose a quantile regression model for the analysis of longitudinal data
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Smoothed model-assisted small area estimation of proportions Can. J. Stat. (IF 0.6) Pub Date : 2023-07-30 Peter A. Gao, Jon Wakefield
In countries where population census data are limited, generating accurate subnational estimates of health and demographic indicators is challenging. Existing model-based geostatistical methods leverage covariate information and spatial smoothing to reduce the variability of estimates but often ignore the survey design, while traditional small area estimation approaches may not incorporate both unit-level
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A calibration method to stabilize estimation with missing data Can. J. Stat. (IF 0.6) Pub Date : 2023-07-30 Baojiang Chen, Ao Yuan, Jing Qin
The augmented inverse weighting (AIW) estimator is commonly used to estimate the marginal mean of an outcome because of its doubly robust property. However, the AIW estimator can be severely biased if both the propensity score (PS) and the outcome regression (OR) models are misspecified. One possible reason is that misspecification of the PS or OR model yields extreme values in these models, which
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Joint modelling of quantile regression for longitudinal data with information observation times and a terminal event Can. J. Stat. (IF 0.6) Pub Date : 2023-07-31 Weicai Pang, Yutao Liu, Xingqiu Zhao, Yong Zhou
Longitudinal data arise frequently in biomedical follow-up observation studies. Conditional mean regression and conditional quantile regression are two popular approaches to model longitudinal data. Many results are derived under the case where the response variables are independent of the observation times. In this article, we propose a quantile regression model for the analysis of longitudinal data
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Smoothed model-assisted small area estimation of proportions Can. J. Stat. (IF 0.6) Pub Date : 2023-07-30 Peter A. Gao, Jon Wakefield
In countries where population census data are limited, generating accurate subnational estimates of health and demographic indicators is challenging. Existing model-based geostatistical methods leverage covariate information and spatial smoothing to reduce the variability of estimates but often ignore the survey design, while traditional small area estimation approaches may not incorporate both unit-level
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A calibration method to stabilize estimation with missing data Can. J. Stat. (IF 0.6) Pub Date : 2023-07-30 Baojiang Chen, Ao Yuan, Jing Qin
The augmented inverse weighting (AIW) estimator is commonly used to estimate the marginal mean of an outcome because of its doubly robust property. However, the AIW estimator can be severely biased if both the propensity score (PS) and the outcome regression (OR) models are misspecified. One possible reason is that misspecification of the PS or OR model yields extreme values in these models, which
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Oscillating neural circuits: Phase, amplitude, and the complex normal distribution Can. J. Stat. (IF 0.6) Pub Date : 2023-07-22 Konrad N. Urban, Heejong Bong, Josue Orellana, Robert E. Kass
Multiple oscillating time series are typically analyzed in the frequency domain, where coherence is usually said to represent the magnitude of the correlation between two signals at a particular frequency. The correlation being referenced is complex-valued and is similar to the real-valued Pearson correlation in some ways but not others. We discuss the dependence among oscillating series in the context
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Oscillating neural circuits: Phase, amplitude, and the complex normal distribution Can. J. Stat. (IF 0.6) Pub Date : 2023-07-22 Konrad N. Urban, Heejong Bong, Josue Orellana, Robert E. Kass
Multiple oscillating time series are typically analyzed in the frequency domain, where coherence is usually said to represent the magnitude of the correlation between two signals at a particular frequency. The correlation being referenced is complex-valued and is similar to the real-valued Pearson correlation in some ways but not others. We discuss the dependence among oscillating series in the context
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On the correlation analysis of stocks with zero returns Can. J. Stat. (IF 0.6) Pub Date : 2023-07-09 Hamdi Raïssi
The purpose of this article is to study serial correlations, allowing for unconditional heteroscedasticity and time-varying probabilities of zero financial returns. Depending on the set-up, we investigate how the standard autocorrelations can be accommodated to deliver an accurate representation of the serial correlations of stock price changes. We shed light on the properties of the different serial
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On the correlation analysis of stocks with zero returns Can. J. Stat. (IF 0.6) Pub Date : 2023-07-09 Hamdi Raïssi
The purpose of this article is to study serial correlations, allowing for unconditional heteroscedasticity and time-varying probabilities of zero financial returns. Depending on the set-up, we investigate how the standard autocorrelations can be accommodated to deliver an accurate representation of the serial correlations of stock price changes. We shed light on the properties of the different serial
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A combined moment equation approach for spatial autoregressive models Can. J. Stat. (IF 0.6) Pub Date : 2023-07-08 Jiaxin Liu, Hongliang Liu, Yi Li, Huazhen Lin
Existing methods for fitting spatial autoregressive models have various strengths and weaknesses. For example, the maximum likelihood estimation (MLE) approach yields efficient estimates but is computationally burdensome. Computationally efficient methods, such as generalized method of moments (GMMs) and spatial two-stage least squares (2SLS), typically require exogenous covariates to be significant
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Analysis of Multivariate Survival Data under Semiparametric Copula Models Can. J. Stat. (IF 0.6) Pub Date : 2023-07-03 Wenqing He, Grace Y. Yi, Ao Yuan
Modelling multivariate survival data is complicated by the complex association structure among the responses. To balance model flexibility and interpretability, we propose a semiparametric copula model to modulate multivariate survival data, with the marginal distributions of the response components described by semiparametric linear transformation models. To conduct inference about the model parameters
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Analysis of Multivariate Survival Data under Semiparametric Copula Models Can. J. Stat. (IF 0.6) Pub Date : 2023-07-03 Wenqing He, Grace Y. Yi, Ao Yuan
Modelling multivariate survival data is complicated by the complex association structure among the responses. To balance model flexibility and interpretability, we propose a semiparametric copula model to modulate multivariate survival data, with the marginal distributions of the response components described by semiparametric linear transformation models. To conduct inference about the model parameters
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Rerandomization and optimal matching Can. J. Stat. (IF 0.6) Pub Date : 2023-07-03 John D. Kalbfleisch, Zhenzhen Xu
On average, randomization achieves balance in covariate distributions between treatment groups; yet in practice, chance imbalance exists post randomization, which increases the error in estimating treatment effects. This is an important issue, especially in cluster randomized trials, where the experimental units (the clusters) are highly heterogeneous and relatively few in number. To address this,
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Rerandomization and optimal matching Can. J. Stat. (IF 0.6) Pub Date : 2023-07-03 John D. Kalbfleisch, Zhenzhen Xu
On average, randomization achieves balance in covariate distributions between treatment groups; yet in practice, chance imbalance exists post randomization, which increases the error in estimating treatment effects. This is an important issue, especially in cluster randomized trials, where the experimental units (the clusters) are highly heterogeneous and relatively few in number. To address this,
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Nonparametric simulation extrapolation for measurement-error models Can. J. Stat. (IF 0.6) Pub Date : 2023-06-27 Dylan Spicker, Michael P. Wallace, Grace Y. Yi
The presence of measurement error is a widespread issue, which, when ignored, can render the results of an analysis unreliable. Numerous corrections for the effects of measurement error have been proposed and studied, often under the assumption of a normally distributed, additive measurement-error model. In many situations, observed data are nonsymmetric, heavy-tailed, or otherwise highly non-normal
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Nonparametric simulation extrapolation for measurement-error models Can. J. Stat. (IF 0.6) Pub Date : 2023-06-27 Dylan Spicker, Michael P. Wallace, Grace Y. Yi
The presence of measurement error is a widespread issue, which, when ignored, can render the results of an analysis unreliable. Numerous corrections for the effects of measurement error have been proposed and studied, often under the assumption of a normally distributed, additive measurement-error model. In many situations, observed data are nonsymmetric, heavy-tailed, or otherwise highly non-normal
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Objective model selection with parallel genetic algorithms using an eradication strategy Can. J. Stat. (IF 0.6) Pub Date : 2023-06-05 Jean-François Plante, Maxime Larocque, Michel Adès
In supervised learning, feature selection methods identify the most relevant predictors to include in a model. For linear models, the inclusion or exclusion of each variable may be represented as a vector of bits playing the role of the genetic material that defines the model. Genetic algorithms reproduce the strategies of natural selection on a population of models to identify the best. We derive
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Objective model selection with parallel genetic algorithms using an eradication strategy Can. J. Stat. (IF 0.6) Pub Date : 2023-06-05 Jean-François Plante, Maxime Larocque, Michel Adès
In supervised learning, feature selection methods identify the most relevant predictors to include in a model. For linear models, the inclusion or exclusion of each variable may be represented as a vector of bits playing the role of the genetic material that defines the model. Genetic algorithms reproduce the strategies of natural selection on a population of models to identify the best. We derive
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Finite sample and asymptotic distributions of a statistic for sufficient follow-up in cure models Can. J. Stat. (IF 0.6) Pub Date : 2023-04-19 Ross Maller, Sidney Resnick, Soudabeh Shemehsavar
The existence of immune or cured individuals in a population and whether there is sufficient follow-up in a sample of censored observations on their lifetimes to be confident of their presence are questions of major importance in medical survival analysis. Here we give a detailed analysis of a statistic designed to test for sufficient follow-up in a sample. Assuming an i.i.d. censoring model, we obtain
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Bayesian instrumental variable estimation in linear measurement error models Can. J. Stat. (IF 0.6) Pub Date : 2023-04-19 Qi Wang, Lichun Wang, Liqun Wang
In this article, we study the problem of parameter estimation for measurement error models by combining the Bayes method with the instrumental variable approach, deriving the posterior distribution of parameters under different priors with known and unknown variance parameters, respectively, and calculating the Bayes estimator (BE) of the parameters under quadratic loss. However, it is difficult to
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New highly efficient high-breakdown estimator of multivariate scatter and location for elliptical distributions Can. J. Stat. (IF 0.6) Pub Date : 2023-04-16 Justin Fishbone, Lamine Mili
High-breakdown-point estimators of multivariate location and shape matrices, such as the MM -SHR with smoothed hard rejection and the Rocke S -estimator, are generally designed to have high efficiency for Gaussian data. However, many phenomena are non-Gaussian, and these estimators can therefore have poor efficiency. This article proposes a new tunable S -estimator, termed the S q -estimator, for the
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Finite sample and asymptotic distributions of a statistic for sufficient follow-up in cure models Can. J. Stat. (IF 0.6) Pub Date : 2023-04-19 Ross Maller, Sidney Resnick, Soudabeh Shemehsavar
The existence of immune or cured individuals in a population and whether there is sufficient follow-up in a sample of censored observations on their lifetimes to be confident of their presence are questions of major importance in medical survival analysis. Here we give a detailed analysis of a statistic designed to test for sufficient follow-up in a sample. Assuming an i.i.d. censoring model, we obtain
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Bayesian instrumental variable estimation in linear measurement error models Can. J. Stat. (IF 0.6) Pub Date : 2023-04-19 Qi Wang, Lichun Wang, Liqun Wang
In this article, we study the problem of parameter estimation for measurement error models by combining the Bayes method with the instrumental variable approach, deriving the posterior distribution of parameters under different priors with known and unknown variance parameters, respectively, and calculating the Bayes estimator (BE) of the parameters under quadratic loss. However, it is difficult to
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New highly efficient high-breakdown estimator of multivariate scatter and location for elliptical distributions Can. J. Stat. (IF 0.6) Pub Date : 2023-04-16 Justin Fishbone, Lamine Mili
High-breakdown-point estimators of multivariate location and shape matrices, such as the MM -SHR with smoothed hard rejection and the Rocke S -estimator, are generally designed to have high efficiency for Gaussian data. However, many phenomena are non-Gaussian, and these estimators can therefore have poor efficiency. This article proposes a new tunable S -estimator, termed the S q -estimator, for the
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A zero-modified geometric INAR(1) model for analyzing count time series with multiple features Can. J. Stat. (IF 0.6) Pub Date : 2023-04-04 Yao Kang, Fukang Zhu, Dehui Wang, Shuhui Wang
Zero inflation, zero deflation, overdispersion, and underdispersion are commonly encountered in count time series. To better describe these characteristics of counts, this article introduces a zero-modified geometric first-order integer-valued autoregressive (INAR(1)) model based on the generalized negative binomial thinning operator, which contains dependent zero-inflated geometric counting series
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A zero-modified geometric INAR(1) model for analyzing count time series with multiple features Can. J. Stat. (IF 0.6) Pub Date : 2023-04-04 Yao Kang, Fukang Zhu, Dehui Wang, Shuhui Wang
Zero inflation, zero deflation, overdispersion, and underdispersion are commonly encountered in count time series. To better describe these characteristics of counts, this article introduces a zero-modified geometric first-order integer-valued autoregressive (INAR(1)) model based on the generalized negative binomial thinning operator, which contains dependent zero-inflated geometric counting series
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A stable and adaptive polygenic signal detection method based on repeated sample splitting Can. J. Stat. (IF 0.6) Pub Date : 2023-03-31 Yanyan Zhao, Lei Sun
Focusing on polygenic signal detection in high-dimensional genetic association studies of complex traits, we develop a stable and adaptive test for generalized linear models to accommodate different alternatives. To facilitate valid post-selection inference for high-dimensional data, our study here adheres to the original sample-splitting principle but does so repeatedly to increase stability of the
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Optimal multiwave validation of secondary use data with outcome and exposure misclassification Can. J. Stat. (IF 0.6) Pub Date : 2023-03-31 Sarah C. Lotspeich, Gustavo G. C. Amorim, Pamela A. Shaw, Ran Tao, Bryan E. Shepherd
Observational databases provide unprecedented opportunities for secondary use in biomedical research. However, these data can be error-prone and must be validated before use. It is usually unrealistic to validate the whole database because of resource constraints. A cost-effective alternative is a two-phase design that validates a subset of records enriched for information about a particular research
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A stable and adaptive polygenic signal detection method based on repeated sample splitting Can. J. Stat. (IF 0.6) Pub Date : 2023-03-31 Yanyan Zhao, Lei Sun
Focusing on polygenic signal detection in high-dimensional genetic association studies of complex traits, we develop a stable and adaptive test for generalized linear models to accommodate different alternatives. To facilitate valid post-selection inference for high-dimensional data, our study here adheres to the original sample-splitting principle but does so repeatedly to increase stability of the
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Optimal multiwave validation of secondary use data with outcome and exposure misclassification Can. J. Stat. (IF 0.6) Pub Date : 2023-03-31 Sarah C. Lotspeich, Gustavo G. C. Amorim, Pamela A. Shaw, Ran Tao, Bryan E. Shepherd
Observational databases provide unprecedented opportunities for secondary use in biomedical research. However, these data can be error-prone and must be validated before use. It is usually unrealistic to validate the whole database because of resource constraints. A cost-effective alternative is a two-phase design that validates a subset of records enriched for information about a particular research
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A class of space-filling designs with low-dimensional stratification and column orthogonality Can. J. Stat. (IF 0.6) Pub Date : 2023-03-28 Pengnan Li, Fasheng Sun
Strong orthogonal arrays are suitable designs for computer experiments because of stratification in low-dimensional projections. However, strong orthogonal arrays may be very expensive for a moderate number of factors. In this article, we develop a method for constructing space-filling designs with more economical run sizes. These designs are not only column-orthogonal but also enjoy a large proportion