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Consistency of the maximum likelihood estimator of population tree in a coalescent framework J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-04-21 Arindam RoyChoudhury
We present a proof of consistency of the maximum likelihood estimator (MLE) of population tree in a previously proposed coalescent model. As the model involves tree-topology as a parameter, the standard proof of consistency for continuous parameters does not directly apply. In addition to proving that a consistent sequence of MLE exists, we also prove that the overall MLE, computed by maximizing the
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Augmented projection Wasserstein distances: Multi-dimensional projection with neural surface J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-04-19 Miyu Sugimoto, Ryo Okano, Masaaki Imaizumi
The Wasserstein distance is a fundamental tool for comparing probability distributions and has found broad applications in various fields, including image generation using generative adversarial networks. Despite its useful properties, the performance of the Wasserstein distance decreases when data is high-dimensional, known as the curse of dimensionality. To mitigate this issue, an extension of the
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Distributed optimal subsampling for quantile regression with massive data J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-04-18 Yue Chao, Xuejun Ma, Boya Zhu
Methods for reducing distributed subsample sizes have increasingly become popular statistical problems in the big data era. Existing works of optimal subsample selection on the massive linear and generalized linear models with distributed data sources have been solidly investigated and widely applied. Nevertheless, few studies have developed distributed optimal subsample selection procedures for quantile
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Entropic regularization of neural networks: Self-similar approximations J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-04-16 Amir R. Asadi, Po-Ling Loh
This paper focuses on entropic regularization and its multiscale extension in neural network learning. We leverage established results that characterize the optimizer of entropic regularization methods and their connection with generalization bounds. To avoid the significant computational complexity involved in sampling from the optimal multiscale Gibbs distributions, we describe how to make measured
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A unified Fourier slice method to derive ridgelet transform for a variety of depth-2 neural networks J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-04-15 Sho Sonoda, Isao Ishikawa, Masahiro Ikeda
To investigate neural network parameters, it is easier to study the distribution of parameters than to study the parameters in each neuron. The ridgelet transform is a pseudo-inverse operator that maps a given function to the parameter distribution so that a network reproduces , i.e. . For depth-2 fully-connected networks on a Euclidean space, the ridgelet transform has been discovered up to the closed-form
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Multiplier subsample bootstrap for statistics of time series J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-04-15 Ruru Ma, Shibin Zhang
Block-based bootstrap, block-based subsampling and multiplier bootstrap are three common nonparametric tools for statistical inference under dependent observations. Combining the ideas of those three, a novel resampling approach, the multiplier subsample bootstrap (MSB), is proposed. Instead of generating a resample from the observations, the MSB imitates the statistic by weighting the block-based
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Convergence guarantees for forward gradient descent in the linear regression model J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-04-06 Thijs Bos, Johannes Schmidt-Hieber
Renewed interest in the relationship between artificial and biological neural networks motivates the study of gradient-free methods. Considering the linear regression model with random design, we theoretically analyze in this work the biologically motivated (weight-perturbed) forward gradient scheme that is based on random linear combination of the gradient. If denotes the number of parameters and
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Toward improved inference for Krippendorff’s Alpha agreement coefficient J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-04-05 John Hughes
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Non-asymptotic model selection for models of network data with parameter vectors of increasing dimension J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-04-05 Sean Eli, Michael Schweinberger
Model selection for network data is an open area of research. Using the -model as a convenient starting point, we propose a simple and non-asymptotic approach to model selection of -models with and without constraints. Simulations indicate that the proposed model selection approach selects the data-generating model with high probability, in contrast to classical and extended Bayesian Information Criteria
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Informed censoring: The parametric combination of data and expert information J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-04-05 Hansjörg Albrecher, Martin Bladt
The statistical censoring setup is extended to the situation when random measures can be assigned to the realization of datapoints, leading to a new way of incorporating expert information into the usual parametric estimation procedures. The asymptotic theory is provided for the resulting estimators, and some special cases of practical relevance are studied in more detail. Although the proposed framework
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Hermite regression estimation in noisy convolution model J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-03-26 Ousmane Sacko
In this paper, we consider the following regression model: , fixed, where is known and is the unknown function to be estimated. The errors are independent and identically distributed centered with finite known variance. Two adaptive estimation methods for are considered by exploiting the properties of the Hermite basis. We study the quadratic risk of each estimator. If belongs to Sobolev regularity
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How many neurons do we need? A refined analysis for shallow networks trained with gradient descent J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-03-26 Mike Nguyen, Nicole Mücke
We analyze the generalization properties of two-layer neural networks in the neural tangent kernel (NTK) regime, trained with gradient descent (GD). For early stopped GD we derive fast rates of convergence that are known to be minimax optimal in the framework of non-parametric regression in reproducing kernel Hilbert spaces. On our way, we precisely keep track of the number of hidden neurons required
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A class of mixed-level uniform designs generated by code mapping J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-03-24 Liuping Hu, Zujun Ou, Hong Qin
Literature reviews reveal that there is a very close connection between experimental design and coding theory. Based on a code mapping transformation, this paper provides a new method to construct a class of mixed designs with two- and four-level. A general construction method is described and some theoretical results of obtained designs are given. Analytic connections are established between the generated
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Robust estimation of a regression function in exponential families J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-03-24 Yannick Baraud, Juntong Chen
We observe pairs of independent (but not necessarily i.i.d.) random variables and tackle the problem of estimating the conditional distributions of given for all . Even though these might not be true, we base our estimator on the assumptions that the data are i.i.d. and the conditional distributions of given belong to a one parameter exponential family with parameter space given by an interval . More
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Measures of conditional dependence for nonlinearity, asymmetry and beyond J. Stat. Plann. Inference (IF 0.9) Pub Date : 2024-03-16 Lianyan Fu, Luyang Zhang
Detecting the correlation between two random variables is widely used in many empirical problems in economics. Among them, Pearson’s correlation can be used to quantify the degree of dependence between variables. However, it cannot handle asymmetric correlations. To deal with this situation, we proposed a pair of widely applicable measures of conditional dependence (), which can not only account for
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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 [formula omitted]-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 : 2024-02-01 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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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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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 and singular asymptotic joint distribution. This estimator is computed faster than the maximum likelihood estimator or the Whittle estimator
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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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