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Bayesian empirical likelihood inference for the mean absolute deviation Statistics (IF 1.9) Pub Date : 2024-03-12 Hongyan Jiang, Yichuan Zhao
The mean absolute deviation (MAD) is a direct measure of the dispersion of a random variable about its mean. In this paper, the empirical likelihood (EL) and the adjusted EL methods for the MAD are...
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Empirical likelihood and estimation in a partially linear varying coefficient model with right censored data Statistics (IF 1.9) Pub Date : 2024-02-25 Liugen G. Xue
In this paper, we study the empirical likelihood and estimation of parameters of interest in a partially linear varying coefficient model with right censored data. Two cases are considered: censori...
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Bernstein-based estimation of the cross ratio function Statistics (IF 1.9) Pub Date : 2024-02-25 Steven Abrams, Ömer Sercik, Noël Veraverbeke
Local association measures provide useful insights in time-varying changes in association, especially between time-to-event variables. Such local dependence between two correlated random variables ...
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Matrix variate receiver operating characteristic curve for binary classification Statistics (IF 1.9) Pub Date : 2024-02-25 G. Siva
In recent years, the study of Receiver Operating Characteristic (ROC) curve analysis has gained significant attention as a means of accurately assessing test performance and determining optimal cut...
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Asymptotic normality of Nadaraya–Waton kernel regression estimation for mixing high-frequency data Statistics (IF 1.9) Pub Date : 2024-02-19 Shanchao Yang, Yanzhe Wang, Lanjiao Qin, Xin Yang
High-frequency data is widely used and studied in many fields, especially in the econometrics and statistics. In this paper, the asymptotic normality of Nadaraya–Waton (NW) kernel regression estima...
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Elegant robustification of sparse partial least squares by robustness-inducing transformations Statistics (IF 1.9) Pub Date : 2024-02-12 Sven Serneels, Luca Insolia, Tim Verdonck
Robust alternatives exist for many statistical estimators. State-of-the-art robust methods are fine-tuned to optimize the balance between statistical efficiency and robustness. The resulting estima...
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Moderate deviations for the mildly stationary autoregressive model with dependent errors Statistics (IF 1.9) Pub Date : 2024-02-04 Hui Jiang, Guangyu Yang, Mingming Yu
In this paper, we consider the normalized least squares estimator of the parameter in a mildly stationary first-order autoregressive (AR(1)) model with dependent errors which are modelled as a mild...
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Survival analysis with censored data: a further twist on ignorability conditions Statistics (IF 1.9) Pub Date : 2024-02-04 Ramon Oller, Guadalupe Gómez Melis
A key assumption for the application of methods concerning censored data is that the random nature of the censoring mechanism should be ignorable when making likelihood-based inferences. The consta...
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A new approach for time domain analysis of multivariate and functional time series Statistics (IF 1.9) Pub Date : 2024-02-04 M. Mohammadpour, S. Rezaee, A. R. Soltani
We apply the classical finite Fourier transform to construct an embedded functional process to a given multivariate time series model. The basic properties of the embedded functional process are pr...
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Detection of long range dependence in the time domain for (in)finite-variance time series Statistics (IF 1.9) Pub Date : 2024-02-04 Marco Oesting, Albert Rapp, Evgeny Spodarev
Empirical detection of long range dependence (LRD) of a time series often consists of deciding whether an estimate of the memory parameter d corresponds to LRD. Surprisingly, the literature offers ...
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Extropy based inaccuracy measure in order statistics Statistics (IF 1.9) Pub Date : 2024-02-04 Morteza Mohammadi, Majid Hashempour
In this paper, we provide a measure based on inaccuracy between distributions of the ith order statistic and the parent random variable. This measure characterizes the distribution function of pare...
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Statistical inference for scale mixture models via Mellin transform approach Statistics (IF 1.9) Pub Date : 2024-01-31 Denis Belomestny, Ekaterina Morozova, Vladimir Panov
This paper deals with statistical inference for the scale mixture models. We study an estimation approach based on the Mellin–Stieltjes transform that can be applied to both discrete and absolute c...
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On conditional spacings and their properties under coherent system setting Statistics (IF 1.9) Pub Date : 2024-01-30 Zhengcheng Zhang, N. Balakrishnan, Ziying Zhao
This paper introduces the concepts of conditional spacings and normalized conditional spacings when an (n−k+1)-out-of-n system or a general coherent system having independent and identically distr...
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Refinement bounds for the expected number of renewal epochs over a finite interval Statistics (IF 1.9) Pub Date : 2024-01-18 Sotirios Losidis
Primary quantities of interest in renewal theory are the expected number of renewals in (0,t], known as renewal function (denoted by U(t)), and the expected number of renewals in (t,t+h], given ...
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Liu-type estimator in Conway–Maxwell–Poisson regression model: theory, simulation and application Statistics (IF 1.9) Pub Date : 2024-01-15 Caner Tanış, Yasin Asar
Recently, many authors have been motivated to propose a new regression estimator in the case of multicollinearity. The most well-known of these estimators are ridge, Liu and Liu-type estimators. Ma...
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Stochastic ordering of variability measure estimators Statistics (IF 1.9) Pub Date : 2024-01-09 Juan Baz, Franco Pellerey, Irene Díaz, Susana Montes
Variability measures, such as the variance or the Gini mean difference, are widely used to summarize the dispersion of random variables. In the statistical setting, it is quite natural to assume th...
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Reliability estimation for Kumaraswamy distribution under block progressive type-II censoring Statistics (IF 1.9) Pub Date : 2024-01-09 Rani Kumari, Yogesh Mani Tripathi, Liang Wang, Rajesh Kumar Sinha
In this article, estimates of reliability performance characteristics are discussed when life test is conducted upon various test facilities under block progressive type-II censoring. When the diff...
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Variance analysis of multiple importance sampling schemes Statistics (IF 1.9) Pub Date : 2023-12-27 Rahul Mukerjee, Víctor Elvira
Multiple importance sampling (MIS) is an increasingly used methodology where several proposal densities are used to approximate integrals, generally involving target probability density functions. ...
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A lower bound of average mixture discrepancy for row augmented designs Statistics (IF 1.9) Pub Date : 2023-12-20 Jiaqi Liu, Kang Wang, Xiaoqing Li, Zujun Ou
Follow-up experimental designs are frequently employed in a wide range of scientific studies and industries. Lower bounds of average mixture discrepancy for row augmented designs are determined in ...
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Universally optimal designs for three interference models under circulant correlation Statistics (IF 1.9) Pub Date : 2023-11-16 Akram Fakhari-Esferizi, Saeid Pooladsaz
In many areas such as agriculture, forestry, and medicine the interference models arise when the response of a treatment is affected by the other treatments in neighbour experimental plots. In this...
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Generalized log-logistic proportional hazard model: a non-penalty shrinkage approach Statistics (IF 1.9) Pub Date : 2023-11-08 Quinn Forzley, Shakhawat Hossain, Shahedul A. Khan
This paper considers the pretest and shrinkage estimation methods for estimating regression parameters of the generalized log-logistic proportional hazard (PH) model. This model is a simple extensi...
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Tuning parameter selection for nonparametric derivative estimation in random design Statistics (IF 1.9) Pub Date : 2023-11-08 Sisheng Liu, Richard Charnigo
Estimation of a function, or its derivatives via nonparametric regression requires selection of one or more tuning parameters. In the present work, we propose a tuning parameter selection criterion...
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Expectile trace regression via low-rank and group sparsity regularization Statistics (IF 1.9) Pub Date : 2023-11-03 Ling Peng, Xiangyong Tan, Peiwen Xiao, Zeinab Rizk, Xiaohui Liu
Trace regression has received a lot of attention due to its ability to account for matrix-type covariates, including panel data, images, and genomic microarrays as special cases. However, most of i...
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An optimization method for change-point monitoring in finite samples sequence Statistics (IF 1.9) Pub Date : 2023-10-13 Dong Han, Fugee Tsung, Jinguo Xian
This article proposes a method of optimizing control chart (sequential test) to detect an abnormal change in a sequence of finite or even small samples with the unknown change-point and the unknown...
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On asymptotic properties of spacings Statistics (IF 1.9) Pub Date : 2023-10-13 Alexandre Berred, Alexei Stepanov
In this work, we investigate spacings based on order statistics obtained from continuous distribution functions. At the beginning of the paper, we present distributional results for spacings and a ...
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Integrated partially linear model for multi-centre studies with heterogeneity and batch effect in covariates Statistics (IF 1.9) Pub Date : 2023-10-13 Lei Yang, Yongzhao Shao
Multi-centre study is increasingly used for borrowing strength from multiple research groups to obtain reproducible study findings. Regression analysis is widely used for analysing multi-group stud...
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Impact assessment of correlated measurement errors using logarithmic-type estimators Statistics (IF 1.9) Pub Date : 2023-10-13 Shashi Bhushan, Anoop Kumar, Shivam Shukla
In survey sampling, several estimation procedures have been proffered by various prominent authors to compute the impact of measurement errors (ME) but the impact of correlated measurement errors (...
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Uniformly strong consistency and the rates of asymptotic normality for the edge frequency polygons Statistics (IF 1.9) Pub Date : 2023-10-10 Mengmei Xi, Chunhua Wang, Xuejun Wang
In this paper, we primarily focus on the edge frequency polygon estimator of f(x), which represents the probability density function of a sequence of φ-mixing random variables {Xi,i≥1}. We establis...
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Asymptotics of lower dimensional zero-density regions Statistics (IF 1.9) Pub Date : 2023-09-25 Hengrui Luo, Steven N. MacEachern, Mario Peruggia
Topological data analysis (TDA) allows us to explore the topological features of a dataset. Among topological features, lower dimensional ones have recently drawn the attention of practitioners in ...
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Automatic selection by penalized asymmetric Lq-norm in a high-dimensional model with grouped variables Statistics (IF 1.9) Pub Date : 2023-09-12 Angelo Alcaraz, Gabriela Ciuperca
The paper focuses on the automatic selection of the grouped explanatory variables in a high-dimensional model, when the model blue error is asymmetric. After introducing the model and notations, we...
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Estimating parameters in multichannel fundamental frequency with harmonics model Statistics (IF 1.9) Pub Date : 2023-09-07 Swagata Nandi, Debasis Kundu
In this paper, we introduce a special multichannel model in the class of multichannel sinusoidal model. In multichannel sinusoidal model, the inherent frequencies from distinct channels are the sam...
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Penalized wavelet nonparametric univariate logistic regression for irregular spaced data Statistics (IF 1.9) Pub Date : 2023-08-29 Umberto Amato, Anestis Antoniadis, Italia De Feis, Irène Gijbels
This paper concerns the study of a non-smooth logistic regression function. The focus is on a high-dimensional binary response case by penalizing the decomposition of the unknown logit regression f...
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On optimal joint prediction of order statistics Statistics (IF 1.9) Pub Date : 2023-08-27 N. Balakrishnan, Rahul Mukerjee
In this paper, we discuss the joint estimation and prediction of unobserved order statistics based on a Type-II censored sample from a location-scale family. Using the concept of Loewner order, we ...
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Corrigendum: a note on Liu-type shrinkage estimations in linear models (Statistics 56, 396–420) Statistics (IF 1.9) Pub Date : 2023-07-31 Sévérien Nkurunziza
ABSTRACT In this paper, we point-out a major error in the proofs of the main results of Bahadır Yüzbaşı, Yasin Asar & S. Ejaz Ahmed [(2022). Liu-type shrinkage estimations in linear models, Statistics, 56:2, 396–420]. In particular, the proofs of their Theorems 3.4–3.5 are based on their Lemma 3.2 which is incorrect.
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Parameter estimation in optional semimartingale regression models Statistics (IF 1.9) Pub Date : 2023-08-01 Alexander Melnikov, Andrey Pak
The paper is devoted to the problem of parameter estimation in a multivariate optional semimartingale regression model. The family of optional semimartingales is a rich class of stochastic processe...
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On general weighted cumulative residual extropy and general weighted negative cumulative extropy Statistics (IF 1.9) Pub Date : 2023-07-31 Santosh Kumar Chaudhary, Nitin Gupta, Pradeep Kumar Sahu
In this paper, we define general weighted cumulative residual extropy (GWCRJ) and general weighted negative cumulative extropy (GWNCJ). We obtain simple estimators for complete and right censored d...
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Robust estimation based on one-shot device test data under log-normal lifetimes Statistics (IF 1.9) Pub Date : 2023-07-26 N. Balakrishnan, E. Castilla
In this paper, we present robust estimators for one-shot device test data under log-normal lifetimes. Based on these estimators, confidence intervals and Wald-type tests are also developed. Their r...
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Characterization-based approach for construction of goodness-of-fit test for Lévy distribution Statistics (IF 1.9) Pub Date : 2023-07-24 Žikica Lukić, Bojana Milošević
The Lévy distribution, alongside the Normal and Cauchy distributions, is one of the only three stable distributions whose density can be obtained in a closed form. However, there are only a few spe...
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Optimal subsampling algorithms for composite quantile regression in massive data Statistics (IF 1.9) Pub Date : 2023-07-24 Jun Jin, Shuangzhe Liu, Tiefeng Ma
Massive datasets have gained increasing prominence across various fields, but their analysis is often impeded by computational limitations. In response, Wang and Ma (Optimal subsampling for quantile regression in big data. Biometrika. 2021;108:99–112) have proposed an optimal subsampling method for quantile regression in massive datasets. Composite quantile regression, as a robust and efficient alternative
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Dimension reduction techniques for conditional expectiles Statistics (IF 1.9) Pub Date : 2023-07-18 Eliana Christou
Marginalizing the importance of characterizing tail events can lead to catastrophic repercussions. Look no further than examples from meteorology and climatology (polar reversals, natural disasters), economics (2008 subprime mortgage crisis), or even medical-diagnostics (low/high risk patients in survival analysis). Investigating these events can become even more challenging when working with high-dimensional
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Fiducialize statistical significance: transforming p-values into conservative posterior probabilities and Bayes factors Statistics (IF 1.9) Pub Date : 2023-07-18 David R. Bickel
One remedy to the misuse of p-values transforms them to bounds on Bayes factors. With a prior probability of the null hypothesis, such a bound gives a lower bound on the posterior probability. Unfortunately, knowing a posterior probability is above some number cannot ensure that the null hypothesis is improbable enough to warrant its rejection. For example, if the lower bound is 0.0001, that implies
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Universal kernel-type estimation of random fields Statistics (IF 1.9) Pub Date : 2023-07-10 Y. Y. Linke, I. S. Borisov, P. S. Ruzankin
Consistent weighted least square estimators are proposed for a wide class of nonparametric regression models with random regression function, where this real-valued random function of k arguments is assumed to be continuous with probability 1. We obtain explicit upper bounds for the rate of uniform convergence in probability of the new estimators to the unobservable random regression function for both
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Weak convergence for weighted sums of a class of random variables with related statistical applications Statistics (IF 1.9) Pub Date : 2023-06-27 Shunping Zheng, Fei Zhang, Chunhua Wang, Xuejun Wang
In this paper, we study the weak convergence and convergence rate in the weak law of large numbers for weighted sums of a class of random variables satisfying the Rosenthal type inequality. The necessary and sufficient conditions for the convergence rates in the weak law of large numbers under some mild conditions are provided. Moreover, the main results that we established are applied to simple linear
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Inference of high quantiles of a heavy-tailed distribution from block data Statistics (IF 1.9) Pub Date : 2023-06-25 Yongcheng Qi, Mengzi Xie, Jingping Yang
In this paper, we consider the estimation problem for high quantiles of a heavy-tailed distribution from block data when only a few largest values are observed within blocks. We propose estimators for high quantiles and prove that these estimators are asymptotically normal. Furthermore, we employ empirical likelihood method and adjusted empirical likelihood method to constructing the confidence intervals
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A Berry–Esseen theorem for sample quantiles under martingale difference sequences Statistics (IF 1.9) Pub Date : 2023-06-20 Chao Lu, Houlin Zhou, Xuejun Wang
In this paper, we establish the uniformly asymptotic normality for sample quantiles based on martingale difference sequences under some suitable conditions. We obtain the rate of normality approximation of O(n−1/4logn) by using some classical methods such as Bernstein type inequality, and so on. Finally, we verify asymptotic normality for the fixed quantile of the martingale difference sequences and
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Zero-inflated binomial integer-valued ARCH models for time series Statistics (IF 1.9) Pub Date : 2023-06-05 Esmeralda Gonçalves, Nazaré Mendes-Lopes
ABSTRACT An integer-valued ARCH process with a conditional zero-inflated binomial distribution is introduced. Stationarity, ergodicity and the autocovariance structure are studied as well as the estimation of parameters by conditional maximum likelihood. Numerical studies and an application to the number of hours in a day in which the prices of electricity for Portugal and Spain are different illustrate
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Extending the Mann–Kendall test to allow for measurement uncertainty Statistics (IF 1.9) Pub Date : 2023-05-28 Stavros Nikolakopoulos, Eric Cator, Mart P. Janssen
The Mann–Kendall test for trend has gained a lot of attention in a range of disciplines, especially in the environmental sciences. One of the drawbacks of the Mann–Kendall test when applied to real data is that no distinction can be made between meaningful and non-meaningful differences in subsequent observations. We introduce the concept of partial ties, which allows inferences while accounting for
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U-Statistics for left truncated and right censored data Statistics (IF 1.9) Pub Date : 2023-05-24 K. K. Sudheesh, S. Anjana, M. Xie
The analysis of left truncated and right censored data is very common in survival and reliability analysis. In lifetime studies patients are often subject to left truncation in addition to right censoring. For example, in bone marrow transplant studies based on International Bone Marrow Transplant Registry (IBMTR), the patients who die while waiting for the transplants will not be reported to the IBMTR
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Convergence in quadratic mean of averaged stochastic gradient algorithms without strong convexity nor bounded gradient Statistics (IF 1.9) Pub Date : 2023-05-17 Antoine Godichon-Baggioni
Online averaged stochastic gradient algorithms are more and more studied since (i) they can deal quickly with large sample taking values in high-dimensional spaces, (ii) they enable to treat data sequentially, (iii) they are known to be asymptotically efficient. In this paper, we focus on giving explicit bounds of the quadratic mean error of the estimates, and this, without supposing that the function
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Some improved results on Berry–Esséen bounds for strong mixing random variables and applications Statistics (IF 1.9) Pub Date : 2023-05-17 Yi Wu, Tien-Chung Hu, Andrei Volodin, Xuejun Wang
In this paper, we mainly establish a general form of Berry–Esséen bound for α-mixing random variables. With different choices of the parameters, the rates are shown as O(n−3/26), O(n−1/6), and approximately O(n−1/4). These results improved some corresponding ones in the literature. An application to the Berry–Esséen bound of sample quantiles is further provided. Moreover, some simulations are also
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Estimating reciprocals of scale parameters of two exponential populations with common location and ordered scales using censored samples Statistics (IF 1.9) Pub Date : 2023-05-15 Mojammel Haque Sarkar, Manas Ranjan Tripathy
The problem of estimating reciprocals of scale parameters (hazard rates) from two exponential populations with a common location and ordered scale parameters have been considered under the progressive type-II censoring scheme from a decision-theoretic viewpoint. The loss function is considered as quadratic. The maximum-likelihood estimators (MLEs) and the uniformly minimum variance unbiased estimators
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High-dimensional rank-based graphical models for non-Gaussian functional data Statistics (IF 1.9) Pub Date : 2023-04-24 Eftychia Solea, Rayan Al Hajj
We study high-dimensional graphical models for non-Gaussian functional data. To relax the Gaussian assumption, we consider the functional Gaussian copula graphical model proposed by Solea and Li [Copula Gaussian graphical models for functional data. J Am Stat Assoc. 2022;117(538):781–793]. To estimate robustly the conditional independence relationships among the functions, we propose a new rank-based
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Sequentially weighted uniform designs Statistics (IF 1.9) Pub Date : 2023-04-27 Yao Xiao, Shiqi Wang, Hong Qin, Jianhui Ning
Uniform designs seek to distribute design points uniformly in the experimental domain. Some discrepancies have been developed to measure the uniformity by treating all factors equally. It is reasonable when there exists no prior information about the system or when the potential model is completely unclear. However, in the situation of sequential designs, experimental information, such as the importance
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Uniformity and projection uniformity of combined designs Statistics (IF 1.9) Pub Date : 2023-04-25 Kang Wang, Hong Qin, Zujun Ou
The purpose of this paper is to study the issue of employing the uniformity criterion measured by the mixture discrepancy to assess the optimal foldover plans for q-level factorials. The average mixture discrepancy and the average projection mixture discrepancy based on the level permutation method are respectively defined for combined designs, and the optimal foldover plan in terms of the overall
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Estimating a new stress–strength index for several exponential populations with a common location Statistics (IF 1.9) Pub Date : 2023-04-24 Tulika Rudra Gupta, Markus Pauly, Somesh Kumar
In design and development of products in various industries, a key characteristic is stress–strength reliability. In this article, we consider estimation of a new stress–strength index for several exponential populations with a common location. We derive various estimators such as the maximum likelihood, the uniformly minimum variance unbiased (UMVU), and Bayes estimators. We additionally apply Brewster–Zidek
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On precedence tests with double sampling Statistics (IF 1.9) Pub Date : 2023-04-24 Niladri Chakraborty, Narayanaswamy Balakrishnan, Maxim Finkelstein
A new double sampling-based precedence and weighted precedence tests are introduced and analysed. The joint distributions of two precedence and weighted precedence statistics are obtained under the double-sampling framework. Subsequently, the closed-form expressions for the rejection probabilities are derived under the null hypothesis and the Lehmann alternative. The corresponding power comparison
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Stochastic comparisons of lifetimes of fail-safe systems with dependent and heterogeneous components under random shocks Statistics (IF 1.9) Pub Date : 2023-04-21 Biplab Hawlader, Pradip Kundu, Amarjit Kundu
A fail-safe system is a (n−1)(n−1) -out-of-n system whose lifetime is represented by the second-order statistic. This work studies stochastic comparisons of lifetimes of fail-safe systems with dependent and heterogeneous components, where the components are subjected to random shocks instantaneously. The results are derived for a general semiparametric family of distributions of the component lifetimes
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Bias reduction estimation for drift coefficient in diffusion models with jumps Statistics (IF 1.9) Pub Date : 2023-04-17 Yuping Song, Hangyan Li
In this paper, we reconstruct the local linear threshold estimator for the drift coefficient of a semimartingale with jumps. Under mild conditions, we provide the asymptotic normality of our estimator in the presence of finite activity jumps whether the underlying process is Harris recurrent or positive recurrent. Simulation studies for different models show that our estimator performs better than
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Unforced errors in the matching problem Statistics (IF 1.9) Pub Date : 2023-04-13 Ignacio Vidal
The solution of the Montmort's matching problem can be seen as the probability mass function of the number of matches in an experiment for assessing the agreement between nominal variables and gold standard classifications. [Vidal I, de Castro M. A Bayesian analysis of the matching problem. J Stat Plan Inference. 2021;212:194–200] presented a generalization of the Montmort's matching problem by considering
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Two-stage communication-efficient distributed sparse M-estimation with missing data Statistics (IF 1.9) Pub Date : 2023-04-12 Xudong Zhang, Ting Zhang, Lei Wang
Distributed estimation based on different sources of observations has drawn attention in the modern statistical learning. When the distributed data are missing at random, we propose a two-stage ℓ1-penalized communication-efficient surrogate likelihood (CSL) algorithm based on inverse probability weighting to eliminate the estimation bias caused by the missing data and construct sparse distributed M-estimator