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Multiple testing in genome-wide association studies via hierarchical hidden Markov models J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-02-29 Pengfei Wang, Zhaofeng Tian
Problems of large-scale multiple testing are often encountered in modern scientific research. Conventional multiple testing procedures usually suffer considerable loss of testing efficiency when correlations among tests are ignored. In fact, appropriate use of correlation information not only enhances the efficacy of the testing procedure, but also improves the interpretability of the results. Since
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Deep learning for ψ-weakly dependent processes J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-02-28 William Kengne, Modou Wade
In this paper, we perform deep neural networks for learning stationary -weakly dependent processes. Such weak-dependence property includes a class of weak dependence conditions such as mixing, association and the setting considered here covers many commonly used situations such as: regression estimation, time series prediction, time series classification The consistency of the empirical risk minimization
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A new approach for ultrahigh dimensional precision matrix estimation J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-02-28 Wanfeng Liang, Yuhao Zhang, Jiyang Wang, Yue Wu, Xiaoyan Ma
The modified Cholesky decomposition (MCD) method is commonly used in precision matrix estimation assuming that the random variables have a specified order. In this paper, we develop a permutation-based refitted cross validation (PRCV) estimation procedure for ultrahigh dimensional precision matrix based on the MCD, which does not rely on the order of variables. The consistency of the proposed estimator
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D4R: Doubly robust reduced rank regression in high dimension J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-02-27 Xiaoyan Ma, Lili Wei, Wanfeng Liang
In this paper, we study high-dimensional reduced rank regression and propose a doubly robust procedure, called , meaning concurrent robustness to both outliers in predictors and heavy-tailed random noise. The proposed method uses the composite gradient descent based algorithm to solve the nonconvex optimization problem resulting from combining Tukey’s biweight loss with spectral regularization. Both
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On card guessing with two types of cards J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-02-16 Markus Kuba, Alois Panholzer
We consider a card guessing strategy for a stack of cards with two different types of cards, say cards of type red (heart or diamond) and cards of type black (clubs or spades). Given a deck of cards, we propose a refined counting of the number of correct colour guesses, when the guesser is provided with complete information, in other words, when the numbers and and the colour of each drawn card are
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Feature screening via concordance indices for left-truncated and right-censored survival data J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-02-10 Li-Pang Chen
Ultrahigh-dimensional data analysis has been a popular topic in decades. In the framework of ultrahigh-dimensional setting, feature screening methods are key techniques to retain informative covariates and screen out non-informative ones when the dimension of covariates is extremely larger than the sample size. In the presence of incomplete data caused by censoring, several valid methods have also
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Scale tests for a multilevel step-stress model with exponential lifetimes under Type-II censoring J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-02-03 Maria Kateri, Nikolay I. Nikolov
Step-stress is a special type of accelerated life-testing procedure that allows the experimenter to test the units of interest under various stress conditions changed (usually increased) at different intermediate time points. In this paper, we study the problem of testing hypothesis for the scale parameter of a simple step-stress model with exponential lifetimes and under Type-II censoring. We consider
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A new non-parametric estimation of the expected shortfall for dependent financial losses J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-02-03 Khouzeima Moutanabbir, Mohammed Bouaddi
In this paper, we address the problem of kernel estimation of the Expected Shortfall (ES) risk measure for financial losses that satisfy the -mixing conditions. First, we introduce a new non-parametric estimator for the ES measure using a kernel estimation. Given that the ES measure is the sum of the Value-at-Risk and the mean-excess function, we provide an estimation of the ES as a sum of the estimators
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Construction of high-dimensional high-separation distance designs J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-02-02 Xu He, Fasheng Sun
Space-filling designs that possess high separation distance are useful for computer experiments. We propose a novel method to construct high-dimensional high-separation distance designs. The construction involves taking the Kronecker product of sub-Hadamard matrices and rotation. In addition to possessing better separation distance than most existing types of space-filling designs, our newly proposed
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Calibrating multi-dimensional complex ODE from noisy data via deep neural networks J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-01-29 Kexuan Li, Fangfang Wang, Ruiqi Liu, Fan Yang, Zuofeng Shang
Ordinary differential equations (ODEs) are widely used to model complex dynamics that arise in biology, chemistry, engineering, finance, physics, etc. Calibration of a complicated ODE system using noisy data is generally challenging. In this paper, we propose a two-stage nonparametric approach to address this problem. We first extract the de-noised data and their higher order derivatives using boundary
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Fast and asymptotically-efficient estimation in an autoregressive process with fractional type noise J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-01-23 Samir Ben Hariz, Alexandre Brouste, Chunhao Cai, Marius Soltane
This paper considers the joint estimation of the parameters of a first-order fractional autoregressive model. A one-step procedure is considered in order to obtain an asymptotically-efficient estimator with an initial guess estimator with convergence speed lower than n and singular asymptotic joint distribution. This estimator is computed faster than the maximum likelihood estimator or the Whittle
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Hilbert space-valued fractionally integrated autoregressive moving average processes with long memory operators J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-01-25 Amaury Durand, François Roueff
Fractionally integrated autoregressive moving average (FIARMA) processes have been widely and successfully used to model and predict univariate time series exhibiting long range dependence. Vector and functional extensions of these processes have also been considered more recently. Here we study these processes by relying on a spectral domain approach in the case where the processes are valued in a
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An empirical likelihood-based unified test for the integer-valued AR(1) models J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-01-26 Jing Zhang, Bo Li, Yu Wang, Xinyi Wei, Xiaohui Liu
In this paper, we suggest an empirical likelihood-based test for the autoregressive coefficient of an integer-valued AR(1) model, i.e., INAR(1). We derive the limit distributions of the resulting test statistic under both null and alternative hypotheses. It turns out that regardless of whether the INAR process is stable or unstable, the statistic is always chi-squared distributed asymptotically under
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On the adaptive Lasso estimator of AR(p) time series with applications to INAR(p) and Hawkes processes J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-01-18 Daniela De Canditiis, Giovanni Luca Torrisi
We investigate the consistency and the rate of convergence of the adaptive Lasso estimator for the parameters of linear AR(p) time series with a white noise which is a strictly stationary and ergodic martingale difference. Roughly speaking, we prove that (i) If the white noise has a finite second moment, then the adaptive Lasso estimator is almost sure consistent (ii) If the white noise has a finite
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Locally adaptive sparse additive quantile regression model with TV penalty J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-01-18 Yue Wang, Hongmei Lin, Zengyan Fan, Heng Lian
High-dimensional additive quantile regression model via penalization provides a powerful tool for analyzing complex data in many contemporary applications. Despite the fast developments, how to combine the strengths of additive quantile regression with total variation penalty with theoretical guarantees still remains unexplored. In this paper, we propose a new methodology for sparse additive quantile
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Statistical inference for wavelet curve estimators of symmetric positive definite matrices J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-01-09 Daniel Rademacher, Johannes Krebs, Rainer von Sachs
In this paper we treat statistical inference for a wavelet estimator of curves of symmetric positive definite (SPD) using the log-Euclidean distance. This estimator preserves positive-definiteness and enjoys permutation-equivariance, which is particularly relevant for covariance matrices. Our second-generation wavelet estimator is based on average-interpolation (AI) and allows the same powerful properties
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A new First-Order mixture integer-valued threshold autoregressive process based on binomial thinning and negative binomial thinning J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-12-26 Danshu Sheng, Dehui Wang, Liuquan Sun
In this paper, we introduce a new first-order mixture integer-valued threshold autoregressive process, based on the binomial and negative binomial thinning operators. Basic probabilistic and statistical properties of this model are discussed. Conditional least squares (CLS) and conditional maximum likelihood (CML) estimators are derived and the asymptotic properties of the estimators are established
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Uniformly more powerful tests for a subset of the components of a Normal Mean Vector J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-12-27 Yining Wang, Gang Li
A class of tests that are uniformly more powerful than the likelihood ratio test is derived for testing the hypothesis about the means of a subset of the components of a multivariate normal distribution with unknown covariance matrix, when the means of the other subset of the components are known.
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Sparse multiple kernel learning: Minimax rates with random projection J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-12-27 Wenqi Lu, Zhongyi Zhu, Rui Li, Heng Lian
In kernel-based learning, the random projection method, also called random sketching, has been successfully used in kernel ridge regression to reduce the computational burden in the big data setting, and at the same time retain the minimax convergence rate. In this work, we consider its use in sparse multiple kernel learning problems where a closed-form optimizer is not available, which poses significant
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Optimal subsampling for the Cox proportional hazards model with massive survival data J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-12-19 Nan Qiao, Wangcheng Li, Feng Xiao, Cunjie Lin
Massive survival data has become common in survival analysis. In this study, a subsampling algorithm is proposed for Cox proportional hazards model with time-dependent covariates when the sample size is extraordinarily large but the computing resources are relatively limited. A subsample estimator is developed by maximizing a weighted partial likelihood, and shown to have consistency and asymptotic
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Adaptively robust high-dimensional matrix factor analysis under Huber loss function J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-12-20 Yinzhi Wang, Yingqiu Zhu, Qiang Sun, Lei Qin
The explosion of data volume and the expansion in data dimensionality have led to a critical challenge in analyzing high-dimensional matrix time series for big data-related applications. In this regard, factor models for matrix-valued high-dimensional time series provide a powerful tool, that reduces the dimensionality of the variables with low-rank structures. However, existing high-dimensional matrix
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Designs for half-diallel experiments with commutative orthogonal block structure J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-12-21 R.A. Bailey, Peter J. Cameron, Dário Ferreira, Sandra S. Ferreira, Célia Nunes
In some experiments, the experimental units are all pairs of individuals who have to undertake a given task together. The set of such pairs forms a triangular association scheme. Appropriate randomization then gives two non-trivial strata. The design is said to have commutative orthogonal block structure (COBS) if the best linear unbiased estimators of treatment contrasts do not depend on the stratum
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Kernel estimation of the transition density in bifurcating Markov chains J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-12-20 S. Valère Bitseki Penda
We study the kernel estimators of the transition density of bifurcating Markov chains. Under some ergodic and regularity properties, we prove that these estimators are consistent and asymptotically normal. Next, in the numerical studies, we propose two data-driven methods to choose the bandwidth parameters. These methods, based on the so-called two bandwidths approach, are adaptation for bifurcating
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Maximum correntropy criterion regression models with tending-to-zero scale parameters J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-12-09 Lianqiang Yang, Ying Jing, Teng Li
Maximum correntropy criterion regression (MCCR) models have been well studied within the theoretical framework of statistical learning when the scale parameters take fixed values or go to infinity. This paper studies MCCR models with tending-to-zero scale parameters. It is revealed that the optimal learning rate of MCCR models is O(n−1) in the asymptotic sense when the sample size n goes to infinity
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Regression analysis of longitudinal data with mixed synchronous and asynchronous longitudinal covariates J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-12-09 Zhuowei Sun, Hongyuan Cao, Li Chen, Jason P. Fine
In linear models, omitting a covariate that is orthogonal to covariates in the model does not result in biased coefficient estimation. This generally does not hold for longitudinal data, where additional assumptions are needed to get an unbiased coefficient estimation in addition to the orthogonality between omitted longitudinal covariates and longitudinal covariates in the model. We propose methods
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Resampling techniques for a class of smooth, possibly data-adaptive empirical copulas J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-12-07 Ivan Kojadinovic, Bingqing Yi
We investigate the validity of two resampling techniques when carrying out inference on the underlying unknown copula using a recently proposed class of smooth, possibly data-adaptive nonparametric estimators that contains empirical Bernstein copulas (and thus the empirical beta copula). Following Kiriliouk et al. (2021), the first resampling technique is based on drawing samples from the smooth estimator
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A global test for heteroscedastic one-way FMANOVA with applications J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-12-05 Tianming Zhu, Jin-Ting Zhang, Ming-Yen Cheng
Multivariate functional data are prevalent in various fields such as biology, climatology, and finance. Motivated by the World Health Data applications, in this study, we propose and examine a global test for assessing the equality of multiple mean functions in multivariate functional data. This test addresses the one-way Functional Multivariate Analysis of Variance (FMANOVA) problem, which is a fundamental
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Inference on regression model with misclassified binary response J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-11-29 Arindam Chatterjee, Tathagata Bandyopadhyay, Ayoushman Bhattacharya
Misclassification of binary responses, if ignored, may severely bias the maximum likelihood estimators (MLEs) of regression parameters. For such data, a binary regression model incorporating non-differential classification errors is extensively used by researchers in different application contexts. We strongly caution against indiscriminate use of this model considering the fact that it suffers from
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A multidimensional objective prior distribution from a scoring rule J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-11-18 Isadora Antoniano-Villalobos, Cristiano Villa, Stephen G. Walker
The construction of objective priors is, at best, challenging for multidimensional parameter spaces. A common practice is to assume independence and set up the joint prior as the product of marginal distributions obtained via “standard” objective methods, such as Jeffreys or reference priors. However, the assumption of independence a priori is not always reasonable, and whether it can be viewed as
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Mallows model averaging based on kernel regression imputation with responses missing at random J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-11-22 Hengkun Zhu, Guohua Zou
Missing data is a common problem in real data analysis. In this paper, a Mallows model averaging method based on kernel regression imputation is proposed for the linear regression models with responses missing at random. We prove that our method asymptotically achieves the lowest possible squared error. Compared with the existing model averaging methods, the new method does not require the use of a
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Construction of mixed-level screening designs using Hadamard matrices J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-11-23 Bo Hu, Dongying Wang, Fasheng Sun
Modern experiments typically involve a very large number of variables. Screening designs allow experimenters to identify active factors in a minimum number of trials. To save costs, only low-level factorial designs are considered for screening experiments, especially two- and three-level designs. In this article, we provide a systematic method to construct screening designs that contain both two- and
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Jackknife empirical likelihood confidence intervals for the categorical Gini correlation J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-11-20 Sameera Hewage, Yongli Sang
The categorical Gini correlation, ρg, was proposed by Dang et al. (2021) to measure the dependence between a categorical variable, Y, and a numerical variable, X. It has been shown that ρg has more appealing properties than current existing dependence measurements. In this paper, we develop the jackknife empirical likelihood (JEL) method for ρg. Confidence intervals for the Gini correlation are constructed
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Variable selection with the knockoffs: Composite null hypotheses J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-11-13 Mehrdad Pournaderi, Yu Xiang
The fixed-X knockoff filter is a flexible framework for variable selection with false discovery rate (FDR) control in linear models with arbitrary design matrices (of full column rank) and it allows for finite-sample selective inference via the Lasso estimates. In this paper, we extend the theory of the knockoff procedure to tests with composite null hypotheses, which are usually more relevant to real-world
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Subgroup analysis for the functional linear model J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-11-15 Yifan Sun, Ziyi Liu, Wu Wang
Classical functional linear regression models the relationship between a scalar response and a functional covariate, where the coefficient function is assumed to be identical for all subjects. In this paper, the classical model is extended to allow heterogeneous coefficient functions across different subgroups of subjects. The greatest challenge is that the subgroup structure is usually unknown to
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Construction of optimal supersaturated designs by the expansive replacement method J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-11-10 Hui Li, Liuqing Yang, Kashinath Chatterjee, Min-Qian Liu
Supersaturated design (SSD) plays an important role in screening factors. E(fNOD) criterion is one of the most widely used criteria to evaluate multi-level and mixed-level SSDs. This paper provides some methods to construct multi-level E(fNOD) optimal SSDs with general run sizes, which can also be extended to construct mixed-level SSDs. The main idea of these methods is combining two processed generalized
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A pair of novel priors for improving and extending the conditional MLE J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-11-10 Takemi Yanagimoto, Yoichi Miyata
A Bayesian estimator aiming at improving the conditional MLE is proposed by introducing a pair of priors. After explaining the conditional MLE by the posterior mode under a prior, we define a promising estimator by the posterior mean under a corresponding prior. The prior is asymptotically equivalent to the reference prior in familiar models. Advantages of the present approach include two different
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Generic E-variables for exact sequential k-sample tests that allow for optional stopping J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-10-26 Rosanne J. Turner, Alexander Ly, Peter D. Grünwald
We develop E-variables for testing whether two or more data streams come from the same source or not, and more generally, whether the difference between the sources is larger than some minimal effect size. These E-variables lead to exact, nonasymptotic tests that remain safe, i.e., keep their type-I error guarantees, under flexible sampling scenarios such as optional stopping and continuation. In special
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A comparison of likelihood-based methods for size-biased sampling J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-10-13 Victoria L. Leaver, Robert G. Clark, Pavel N. Krivitsky, Carole L. Birrell
Three likelihood approaches to estimation under informative sampling are compared using a special case for which analytic expressions are possible to derive. An independent and identically distributed population of values of a variable of interest is drawn from a gamma distribution, with the shape parameter and the population size both assumed to be known. The sampling method is selection with probability
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Maximum likelihood estimation of the log-concave component in a semi-parametric mixture with a standard normal density J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-10-07 Fadoua Balabdaoui, Harald Besdziek
The two-component mixture model with known background density, unknown signal density, and unknown mixing proportion has been studied in many contexts. One such context is multiple testing, where the background and signal densities describe the distribution of the p-values under the null and alternative hypotheses, respectively. In this paper, we consider the log-concave MLE of the signal density using
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Regression models for circular data based on nonnegative trigonometric sums J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-09-27 Juan José Fernández-Durán, María Mercedes Gregorio-Domínguez
The parameter space of nonnegative trigonometric sums (NNTS) models for circular data is the surface of a hypersphere; thus, constructing regression models for a circular-dependent variable using NNTS models can comprise fitting great (small) circles on the parameter hypersphere that can identify different regions (rotations) along the great (small) circle. We propose regression models for circular-
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Time changes and stationarity issues for extended scalar autoregressive models J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-09-23 V. Girardin, R. Senoussi
A scalar discrete or continuous time process is reducible to stationarity (RWS) if its transform by some smooth time change is weakly stationary. Different issues linked to this notion are here investigated for autoregressive (AR) models. AR models are understood in a large sense and may have time-varying coefficients. In the continuous time case the innovation may be of the semi-martingale type–such
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Testing higher and infinite degrees of stochastic dominance for small samples: A Bayesian approach J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-09-17 Mariusz Górajski
This study proposes a distribution-free Bayesian procedure that detects infinite degrees of stochastic dominance (SD∞) between two random outcomes and then seeks a finite degree k≥1 of stochastic dominance (SDk). Based on small samples, we construct four-choice Bayesian tests by combining an encompassing prior Bayesian model with the Dirichlet process priors that discriminate between SD∞ and SDk of
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Z-valued time series: Models, properties and comparison J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-08-25 Qi Li, Huaping Chen, Fukang Zhu
This paper devotes to give a comprehensive review of Z-valued time series models, which allow negative autocorrelations besides positive autocorrelations. Z-valued versions of integer-valued autoregressive (INAR) models are mainly based on three different operators: signed thinning operators, difference operators and rounding operators, while Z-valued versions of integer-valued generalized autoregressive
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Optimal model averaging for semiparametric partially linear models with measurement errors J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-08-24 Guozhi Hu, Weihu Cheng, Jie Zeng, Ruijie Guan
This paper is concerned with optimal model averaging procedure for semiparametric partially linear models where some covariates are subject to measurement error. We proposed a corrected semiparametric generalized least squares estimation for unknown parameters and nonparametric function, and developed a Mallows-type criterion for weight choice. The resulting model average estimator is shown to be asymptotically
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Robust inference for subgroup analysis with general transformation models J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-08-24 Miao Han, Yuanyuan Lin, Wenxin Liu, Zhanfeng Wang
A crucial step in developing personalized strategies in precision medicine or precision marketing is to identify the latent subgroups of patients or customers of a heterogeneous population. In this article, we consider a general class of heterogeneous transformation models for subgroup identification, under which an unknown monotonic transformation of the response is linearly related to the covariates
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A framework of zero-inflated bayesian negative binomial regression models for spatiotemporal data J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-08-22 Qing He, Hsin-Hsiung Huang
Spatiotemporal data analysis with massive zeros is widely used in many areas such as epidemiology and public health. We use a Bayesian framework to fit zero-inflated negative binomial models and employ a set of latent variables from Pólya-Gamma distributions to derive an efficient Gibbs sampler. The proposed model accommodates varying spatial and temporal random effects through Gaussian process priors
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Information content of stepped wedge designs under the working independence assumption J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-08-19 Zibo Tian, Fan Li
The stepped wedge design is increasingly popular in pragmatic trials and implementation science research studies for evaluating system-level interventions that are perceived to be beneficial to patient populations. An important step in planning a stepped wedge design is to understand the efficiency of the treatment effect estimator and hence the power of the study. We develop several novel analytical
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A smoothed p-value test when there is a nuisance parameter under the alternative J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-08-09 Jonathan B. Hill
We present a new test when there is a nuisance parameter λ under the alternative hypothesis. The test exploits the p-value occupation time [PVOT], the measure of the subset of λ on which a p-value test based on a test statistic Tn(λ) rejects the null hypothesis. Key contributions are: (i) An asymptotic critical value upper bound for our test is the significance level α, making inference easy. (ii)
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Zero-modified count time series with Markovian intensities J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-08-05 N. Balakrishna, P. Muhammed Anvar
This paper proposes a method for analyzing count time series with inflation or deflation of zeros. In particular, zero-modified Poisson and zero-modified negative binomial series with intensities generated by non-negative Markov sequences are studied in detail. Parameters of the model are estimated by the method of estimating equations which is facilitated by expressing the model in a generalized state
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Statistical inference in factor analysis for diffusion processes from discrete observations J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-08-03 Shogo Kusano, Masayuki Uchida
In behavioral science, there are many studies of factor analysis for time series data. These studies assumed a discrete-time stochastic process model. Since the development of measuring devices enables us to obtain high-frequency data, factor analysis based on high-frequency data has become important. In financial econometrics, principal component analysis can estimate the factor model for high-frequency
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Further results on controlling the false discovery rate under some complex grouping structure of hypotheses J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-08-03 Shinjini Nandi, Sanat K. Sarkar
This article considers developing false discovery rate (FDR) controlling methods for testing multiple hypotheses under three different classification settings of the hypotheses into groups — simultaneous multi-way classification, hierarchical classification, and a combination of these two classifications. The methods are developed in their oracle forms by considering a weighted version of the Benjamini–Hochberg
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Fast construction of efficient two-level parallel flats designs J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-08-02 Chunyan Wang, Robert W. Mee
Two-level parallel flats designs (PFDs) are nonregular designs that retain some of the simplicity of regular fractional factorial designs. PFDs have many desirable properties, such as flexible run sizes and block diagonal information matrices. In this article, we develop a fast algorithm for constructing efficient two-level PFDs, optimizing the parallel flats structure via a novel application of coordinate
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Regression-assisted Bayesian record linkage for causal inference in observational studies with covariates spread over two files J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-08-01 Sharmistha Guha, Jerome P. Reiter
We consider causal inference for observational studies with data spread over two files. One file includes the treatment, outcome, and some covariates measured on a set of individuals, and the other file includes additional causally-relevant covariates measured on a partially overlapping set of individuals. By linking records in the two databases, the analyst can control for more covariates, thereby
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Confidence intervals for a ratio of percentiles of location-scale distributions J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-07-29 K. Krishnamoorthy, Saptarshi Chakraberty
The problem of estimating a ratio of percentiles of two independent location-scale distributions is considered. A fiducial approach is proposed and described in details for the normal, lognormal, two-parameter exponential and Weibull distributions. For the normal case, the fiducial confidence intervals (CIs) turn out to be exact when the variances are equal. Procedures for constructing CIs for ratio
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Uniformly valid inference for partially linear high-dimensional single-index models J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-07-23 Pieter Willems, Gerda Claeskens
Uniformly valid inference is obtained for the linear part of a partially linear single-index model with a high-dimensional variable in the single-index part of the model. The linear model part consists of a fixed and limited number of variables, while the control variables in the single-index might consist of a large number of variables that is allowed to grow with the sample size and potentially exceeds
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Inference for seemingly unrelated linear mixed models J. Stat. Plann. Inference (IF 0.9) Pub Date : 2023-07-13 Lichun Wang, Yang Yang
Linear mixed models and data generated from repeated measurements have found substantial applications in many disciplines. This paper considers the estimation problem of fixed effects and variance components in the system of two seemingly unrelated linear mixed models. We first propose the covariance adjustment estimator of the fixed effects and then construct consistent estimators of its unknown parameters