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Variable selection in the Box–Cox power transformation model J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210512
Baojiang Chen, Jing Qin, Ao YuanHigh dimensional data are frequently collected across research fields such as genomics, health sciences, economics, and social sciences. Recently, variable selection in the high dimensional setting has drawn great attention, with many effective methods developed to reduce the dimensionality of the data. However, most of these methods apply only to normally or near normally distributed outcomes in a

Semiparametric inference for merged data from multiple data sources J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210512
Takumi SaegusaWe consider general semiparametric inference when data are merged from multiple overlapping sources. Merged data exhibit several characteristics including heterogeneity across multiple data sources, potential unidentified duplicated records, and dependence due to sampling without replacement within each data source. In this paper, we establish a large sample theory for the weighted semiparametric Mestimation

Nonparametric inference for distribution functions with stratified samples J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210507
Takumi SaegusaWe consider nonparametric estimation of a distribution function when data are collected from twophase stratified sampling without replacement. We study the inverse probability weighted empirical distribution function and propose a novel computational procedure to construct a confidence band. Twophase sampling design induces heterogeneity across strata and dependence due to sampling without replacement

Twoway ORDANOVA: Analyzing ordinal variation in a crossbalanced design J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210428
Tamar Gadrich, Yariv N. MarmorVariability assessment of qualitative data has an important role in diverse areas such as sociology, quality engineering, healthcare, decision making, genetics, metrology in chemistry and many others. To test the homogeneity hypothesis, we use two main categorical factors. Using a crossbalanced design, we provide a decomposition theorem of the sample totalvariation into ‘intra’ (within) component

Ksign depth: From asymptotics to efficient implementation J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210428
Dennis Malcherczyk, Kevin Leckey, Christine H. MüllerThe Ksign depth (Kdepth) of a model parameter θ in a data set is the relative number of Ktuples among its residual vector that have alternating signs. The Kdepth test based on Kdepth, recently proposed by Leckey et al. (2020), is equivalent to the classical residualbased sign test for K=2, but is much more powerful for K≥3. This test has two major drawbacks. First, the computation of the Kdepth

Maximum likelihood estimation of sparse networks with missing observations J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210424
Solenne Gaucher, Olga KloppEstimating the matrix of connections probabilities is one of the key questions when studying sparse networks. In this work, we consider networks generated under the sparse graphon model and the inhomogeneous random graph model with missing observations. Using the Stochastic Block Model as a parametric proxy, we bound the risk of the maximum likelihood estimator of network connections probabilities

A sequential approach to feature selection in highdimensional additive models J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210424
Yuan Gong, Zehua ChenWe deal with the problem of feature selection for highdimensional additive models in this article. The existing feature selection methods for additive models in the literature mainly concentrate on the penalized likelihood approach. We propose a sequential group selection method for additive models (sgsAM) in this article. The additive functions are estimated by Bspline method and are treated as

Density estimation of a mixture distribution with unknown pointmass and normal error J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210418
Dang Duc Trong, Nguyen Hoang Thanh, Nguyen Dang Minh, Nguyen Nhu LanWe consider the model Y=X+ξ where Y is observable, ξ is a noise random variable with density fξ, X has an unknown mixed density such that P(X=Xc)=1−p, P(X=a)=p with Xc being continuous and p∈(0,1), a∈R. Typically, in the last decade, the model has been widely considered in a number of papers for the case of fully known quantities a,fξ. In this paper, we relax the assumptions and consider the parametric

Estimation of the inverse scatter matrix for a scale mixture of Wishart matrices under EfronMorris type losses J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210421
Djamila Boukehil, Dominique Fourdrinier, Fatiha Mezoued, William E. StrawdermanWe consider estimation of the inverse scatter matrix Σ−1 for a scale mixture of Wishart matrices under various EfronMorris type losses, tr[{Σˆ−1−Σ−1}2Sk] for k=0,1,2..., where S is the sample covariance matrix. We improve on the standard estimators aS+, where S+ denotes the Moore–Penrose inverse of S and a is a positive constant, through an unbiased estimator of the risk difference between the new

Efficient tapered local Whittle estimation of multivariate fractional processes J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210402
Masaki NarukawaThe semiparametric estimation of multivariate fractional processes based on the tapered periodogram of the differenced series is considered in this paper. We construct multivariate local Whittle estimators by incorporating the maximal efficient taper developed by Chen (2010). The proposed estimation method allows a wide range of potentially nonstationary longrange dependent series, being invariant

An integrative framework for geometric and hidden projections in threelevel fractional factorial designs J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210329
Arman SabbaghiGeometric and hidden projection are two important properties to consider for threelevel fractional factorials. The effective development of methods that can address tasks involving both these properties requires a unified design framework that integrates them. We highlight one integrative framework that is based on indicator functions for threelevel designs under the linearquadratic parameterization

A precise local limit theorem for the multinomial distribution and some applications J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210401
Frédéric OuimetIn Siotani and Fujikoshi (1984), a precise local limit theorem for the multinomial distribution is derived by inverting the Fourier transform, where the error terms are explicit up to order N−1. In this paper, we give an alternative (conceptually simpler) proof based on Stirling’s formula and a careful handling of Taylor expansions, and we show how the result can be used to approximate multinomial

The limits of the sample spiked eigenvalues for a highdimensional generalized Fisher matrix and its applications J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210326
Dandan Jiang, Zhiqiang Hou, Jiang HuA generalized spiked Fisher matrix is considered in this paper. We establish a criterion for the description of the support of the limiting spectral distribution of highdimensional generalized Fisher matrix and study the almost sure limits of the sample spiked eigenvalues where the population covariance matrices are arbitrary which successively removed an unrealistic condition posed in the previous

Doptimal designs for estimation of parameters in a simplex dispersion model with proportional data J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210322
HsiangLing Hsu, MongNa Lo HuangIn this work, optimal design problems for estimation of unknown parameters for a flexible class of nonnormal distributions useful for describing various data types are considered. A particular model, designated the simplex dispersion model, can be applied to model proportional (or compositional) outcomes confined within the (0, 1) interval. The main interest here is to determine the optimal experimental

Columnorthogonal nearly strong orthogonal arrays J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210319
Wenlong Li, MinQian Liu, JianFeng YangStrong orthogonal arrays enjoy more attractive spacefilling properties than ordinary orthogonal arrays for computer experiments. In this paper, we propose two methods for constructing columnorthogonal nearly strong orthogonal arrays. These designs enjoy column orthogonality, inherit the attractive twodimensional spacefilling property of strong orthogonal arrays, and can accommodate twice or more

Estimating diffusion with compound Poisson jumps based on selfnormalized residuals J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210305
Hiroki Masuda, Yuma UeharaWe consider parametric estimation of the continuous part of a class of ergodic diffusions with jumps based on highfrequency samples. Various papers previously proposed threshold based methods, which enable us to distinguish whether observed increments have jumps or not at each smalltime interval, hence to estimate the unknown parameters separately. However, a dataadapted and quantitative choice

Bis∗concave distributions J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210313
Nilanjana Laha, Zhen Miao, Jon A. WellnerWe introduce new shapeconstrained classes of distribution functions on R, the bis∗concave classes. In parallel to results of Dümbgen et al. (2017) for what they called the class of bilogconcave distribution functions, we show that every sconcave density f has a bis∗concave distribution function F for s∗≤s∕(s+1). Confidence bands building on existing nonparametric confidence bands, but accounting

A test for secondorder stationarity of a time series based on the maximum of Anderson–Darling statistics J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210309
Shibin ZhangThis paper is concerned with testing the secondorder stationarity of a time series. By using a blockwise scheme, the test is transformed to compare local spectra of different segments of the blocked time series. Based on periodogramratios of each pair of segments at the same frequency points, an Anderson–Darlinglike statistic is constructed to compare their spectra. By maximizing several Anderson–Darlinglike

Anisotropic multivariate deconvolution using projection on the Laguerre basis J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210222
Florian DussapWe investigate adaptive density estimation in the additive model Z=X+Y, where X and Y are independent ddimensional random vectors with nonnegative coordinates. Our goal is to recover the density of X from independent observations of Z, assuming the density of Y is known. In the d=1 case, an estimation procedure using projection on the Laguerre basis has already been studied. We generalize this procedure

Improved variance estimation for inequalityconstrained domain mean estimators using survey data J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210223
Xiaoming Xu, Mary C. Meyer, Jean D. OpsomerIn survey domain estimation, a priori information can often be imposed in the form of linear inequality constraints on the domain estimators. Wu et al. (2016) formulated the isotonic domain mean estimator, for the simple order restriction, and methods for more general constraints were proposed in OlivaAvilés et al. (2020). When the assumptions are valid, imposing restrictions on the estimators will

Testing hypothesis on transition distributions of a Markov sequence J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210224
Estate V. KhmaladzeWe propose a method for testing hypothesis on parametric family of transition probabilities of a Markov sequence, when the asymptotic distribution of the empirical processes involved is, largely, independent from the specific form of the parametric family. We first consider functionparametric empirical process for the Markov sequence and describe its weak limit as a certain projection. Then we establish

On optimal subset designs for phase II clinical trials with both total response and disease control J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210227
Guijun Yang, Jingbo Yang, Xue Yang, Weizhen WangPhase II clinical trials in oncology are used to initially evaluate the therapeutic efficacy of a new treatment. In the past, the total response was a frequently used endpoint to access the effectiveness of the treatment. When the total response is modest, clinicians may also be interested in disease control (defined as the total response or stable disease) since it may better predict clinical outcomes

Adapting to one and twoway classified structures of hypotheses while controlling the false discovery rate J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210227
Shinjini Nandi, Sanat K. Sarkar, Xiongzhi ChenThere is ample research on false discovery rate (FDR) control for testing hypotheses classified according to one criterion. However, scenarios of hypotheses partitioned via two different criteria are often encountered in practice. Such twoway classification encodes more structural information in the associated multiple testing of the hypotheses than its oneway or unclassified counterparts. Unfortunately

On IPWbased estimation of conditional average treatment effects J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210218
Niwen Zhou, Lixing ZhuThe research in this paper gives a systematic investigation of the asymptotic behaviors of four inverse probability weighting (IPW)based estimators for conditional average treatment effects, with nonparametrically, semiparametrically, parametrically estimated, and true propensity score, respectively. To this end, we first pay particular attention to semiparametric dimension reduction structure such

A modification of MaxT procedure using spurious correlations J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210217
Yoshiyuki Ninomiya, Satoshi Kuriki, Toshihiko Shiroishi, Toyoyuki TakadaWe consider one of the most basic multiple testing problems that compares expectations of multivariate data among several groups. As a test statistic, a conventional (approximate) tstatistic is considered, and we determine its rejection region using a common rejection limit. When there are unknown correlations among test statistics, the multiplicity adjusted pvalues are dependent on the unknown correlations

The effects of adaptation on maximum likelihood inference for nonlinear models with normal errors J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210213
Nancy Flournoy, Caterina May, Chiara TommasiThis work studies the properties of the maximum likelihood estimator (MLE) of a multidimensional parameter in a nonlinear model with additive Gaussian errors. The observations are collected in a twostage experimental design and are dependent because the second stage design is determined by the observations at the first stage. The MLE maximizes the total likelihood. Unlike most theory in the literature

Validation likelihood estimation method for a zeroinflated Bernoulli regression model with missing covariates J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210211
ShenMing Lee, KimHung Pho, ChinShang LiA zeroinflated Bernoulli (ZIBer) regression model is an alternative model in order to improve fitting the binary outcome data that have many more zeros than expected under a regular logistic regression model. Because some covariates often have missing values, we propose the validation likelihood (VL) method to estimate the parameters of the ZIBer regression model with covariates missing at random

Asymptotic results with estimating equations for timeevolving clustered data J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210126
Laura Dumitrescu, Ioana SchiopuKratinaWe study the existence, strong consistency and asymptotic normality of estimators obtained from estimating functions, that are pdimensional martingale transforms. The problem is motivated by the analysis of evolutionary clustered data, with distributions belonging to the exponential family, and which may also vary in terms of other component series. Within a quasilikelihood approach, we construct

Estimation in functional singleindex varying coefficient model J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210127
Sanying Feng, Ping Tian, Yuping Hu, Gaorong LiFunctional regression allows for a scalar response to be dependent on a functional predictor, however, not much work has been done when scalar predictors that interacts with the functional predictor are introduced. In this paper, we introduce a new functional singleindex varying coefficient model with the functional predictor being singleindex part. By means of functional principal components analysis

Sparse designs for estimating variance components of nested factors with random effects J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210120
R.A. Bailey, Célia Fernandes, Paulo RamosA new class of designs is introduced for both estimating the variance components of nested factors and testing hypotheses about those variance components. These designs are flexible, and can be chosen so that the degrees of freedom are more evenly spread among the factors than they are in balanced nested designs. The variances of the estimators are smaller than those in stair nested designs of comparable

Semiparametric homogeneity test and sample size calculation for a twosample problem under an inequality constraint J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210126
Guanfu Liu, Yan Fan, Yang Liu, Yukun LiuIn medical researches such as casecontrol studies with contaminated controls, frequently encountered is a particular twosample testing problem in which one sample has a mixture structure. It is a very common case that the exposure in a casecontrol study may have a positive (or negative) effect on the response variable if the effect exists. This is often ignored by existing tests, which would lead

A new user specific multiple testing method for business applications: The SiMaFlex procedure J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210127
Christina C. Bartenschlager, Jens O. BrunnerMultiple hypotheses testing problems are highly relevant whenever data is evaluated statistically. In business research, for example, the topic gains more importance due to the increase in data driven approaches for efficient decision making. By now, the decision on a suitable multiple hypotheses method presupposes a twostep selection. First, the user has to decide on the multiple type I error definition

Fitting time series models for longitudinal surveys with nonignorable missing data J. Stat. Plann. Inference (IF 0.679) Pub Date : 20210120
Zhan Liu, Chun Yip YauIn this paper, we develop a method for handling nonignorable missing data in fitting time series models for longitudinal surveys. We assume that the response probability not only depends on auxiliary variables but also the current and past outcomes which are subject to missingness. Under a nonignorable missing mechanism, an observed likelihood estimation approach is proposed based on the distribution

Uniform semiLatin squares and their pairwisevariance aberrations J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201219
R.A. Bailey, Leonard H. SoicherFor integers n>2 and k>0, an (n×n)∕k semiLatin square is an n×n array of ksubsets (called blocks) of an nkset (of treatments), such that each treatment occurs once in each row and once in each column of the array. A semiLatin square is uniform if every pair of blocks, not in the same row or column, intersect in the same positive number of treatments. It is known that a uniform (n×n)∕k semiLatin

A scalable surrogate L0 sparse regression method for generalized linear models with applications to large scale data J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201217
Ning Li, Xiaoling Peng, Eric Kawaguchi, Marc A. Suchard, Gang LiThis paper rigorously studies large sample properties of a surrogate L0 penalization method via iteratively performing reweighted L2 penalized regressions for generalized linear models and develop a scalable implementation of the method for sparse high dimensional massive sample size (sHDMSS) data. We show that for generalized linear models, the limit of the algorithm, referred to as the broken adaptive

Optimal crossover designs for inference on total effects J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201214
Suja Aboukhamseen, Shahariar Huda, Mausumi BoseCrossover designs involve two types of treatment effects, a direct effect and a carryover effect, and several optimality results are available for inferring on these two effects separately. However, an aim of a designed experiment is to recommend a single treatment which will be used over longer time periods. When this treatment is used over many periods, the effect on the subject at any time period

Bregman divergence to generalize Bayesian influence measures for data analysis J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201208
Melaine C. De Oliveira, Luis M. Castro, Dipak K. Dey, Debajyoti SinhaFor existing Bayesian crossvalidated measure of influence of each observation on the posterior distribution, this paper considers a generalization using the Bregman Divergence (BD). We investigate various practically useful and desirable properties of these BD based measures to demonstrate the superiority of these measures compared to existing Bayesian measures of influence and Bayesian residual based

Removing inessential points in cand Aoptimal design J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201208
Luc Pronzato, Guillaume SagnolA design point is inessential when it does not contribute to an optimal design, and can therefore be safely discarded from the design space. We derive three inequalities for the detection of such inessential points in coptimal design: the first two are direct consequences of the equivalence theorem for coptimality; the third one is derived from a secondorder cone programming formulation of coptimal

Higherorder approximate confidence intervals J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201205
Eliane C. Pinheiro, Silvia L.P. Ferrari, Francisco M.C. MedeirosStandard confidence intervals employed in applied statistical analysis are usually based on asymptotic approximations. Such approximations can be considerably inaccurate in small and moderate sized samples. We derive accurate confidence intervals based on higherorder approximate quantiles of the score function. The coverage approximation error is O(n−3∕2) while the approximation error of confidence

Bayesian change point detection for functional data J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201203
Xiuqi Li, Subhashis GhosalWe propose a Bayesian method to detect change points in a sequence of functional observations that are signal functions observed with noises. Since functions have unlimited features, it is natural to think that the sequence of signal functions driving the underlying functional observations change through an evolution process, that is, different features change over time but possibly at different times

Estimation of a distribution function with increasing failure rate average J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201112
Hammou El Barmi, Ganesh Malla, Hari MukerjeeA life distribution function F is said to have an increasing failure rate average if H(x)∕x is nondecreasing where H(x) is the corresponding cumulative hazard function. In this paper we provide a uniformly strongly consistent estimator of F and derive the convergence in distribution of the estimator at a point where H(x)∕x is increasing using the arg max theorem. We also show using simulations that

Reference priors via αdivergence for a certain nonregular model in the presence of a nuisance parameter J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201129
Shintaro HashimotoThis paper presents reference priors for nonregular model whose support depends on an unknown parameter. A multiparameter family which includes both regular and nonregular structures is considered. The resulting priors are obtained by asymptotically maximizing the expected αdivergence between the prior and the corresponding posterior distribution. Some examples of reference priors for typical multiparameter

Invariant density adaptive estimation for ergodic jump–diffusion processes over anisotropic classes J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201127
Chiara Amorino, Arnaud GloterWe consider the solution X=(Xt)t≥0 of a multivariate stochastic differential equation with Levytype jumps and with unique invariant probability measure with density μ. We assume that a continuous record of observations XT=(Xt)0≤t≤T is available. In the case without jumps, Dalalyan and Reiss (2007) and Strauch (2018) have found convergence rates of invariant density estimators, under respectively isotropic

Twosample Behrens–Fisher problems for highdimensional data: A normal reference approach J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201127
JinTing Zhang, Bu Zhou, Jia Guo, Tianming ZhuHighdimensional data are frequently encountered with the development of modern data collection techniques. Testing the equality of the mean vectors of two highdimensional samples with possibly different covariance matrices is usually referred to as a highdimensional twosample Behrens–Fisher (BF) problem. In the highdimensional setting, the classical BF solutions are expected to perform poorly

Construction of spacefilling orthogonal designs J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201127
Chunyan Wang, Jinyu Yang, MinQian LiuFor designs of computer experiments, columnorthogonality and spacefilling property are two desirable properties. In this paper, we develop methods for constructing a new class of designs that include orthogonal Latin hypercube designs as special cases. These designs are not only columnorthogonal but also have good spacefilling properties in low dimensions. All these appealing properties make them

Simultaneous inference of the partially linear model with a multivariate unknown function J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201121
Kun Ho Kim, ShihKang Chao, Wolfgang K. HärdleIn this paper, we conduct simultaneous inference of the nonparametric part of a partially linear model when the nonparametric component is a multivariate unknown function. Based on semiparametric estimates of the model, we construct a simultaneous confidence region of the multivariate function for simultaneous inference. The developed methodology is applied to perform simultaneous inference for

Construction of component orthogonal arrays with any number of components J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201114
Hengzhen HuangComponent orthogonal arrays, as fractional designs of all possible permutations on experimental factors, are suitable for orderofaddition experiments due to their pairwise balance in any two positions of the orders. The existing component orthogonal arrays are mainly restricted to the case where the number of components is prime power. In this paper, we construct component orthogonal arrays with

On expectileassisted inverse regression estimation for sufficient dimension reduction J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201120
AbdulNasah Soale, Yuexiao DongMomentbased sufficient dimension reduction methods such as sliced inverse regression may not work well in the presence of heteroscedasticity. We propose to first estimate the expectiles through kernel expectile regression, and then carry out dimension reduction based on random projections of the regression expectiles. Several popular inverse regression methods in the literature are extended under

An orthogonally equivariant estimator of the covariance matrix in high dimensions and for small sample sizes J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201116
Samprit Banerjee, Stefano MonniWe introduce an estimation method of covariance matrices in a highdimensional setting, i.e., when the dimension of the matrix, p, is larger than the sample size n. Specifically, we propose an orthogonally equivariant estimator. The eigenvectors of such estimator are the same as those of the sample covariance matrix. The eigenvalue estimates are obtained from an adjusted profile likelihood function

Optimal and efficient designs for fMRI experiments via twolevel circulant almost orthogonal arrays J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201121
XiaoNan Lu, Miwako Mishima, Nobuko Miyamoto, Masakazu JimboIn this paper, we investigate a class of optimal circulant {0,1}arrays other than the previously known class of optimal designs for fMRI experiments with a single type of stimulus. We suppose throughout the paper that n≡2(mod4) and discuss the asymptotic optimality and the Defficiency of k×n circulant almost orthogonal arrays (CAOAs) with 2 levels (presence/absence of the stimulus), strength 2 and

Optimal sparse eigenspace and lowrank density matrix estimation for quantum systems J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201117
Tony Cai, Donggyu Kim, Xinyu Song, Yazhen WangQuantum state tomography, which aims to estimate quantum states that are described by density matrices, plays an important role in quantum science and quantum technology. This paper examines the eigenspace estimation and the reconstruction of large lowrank density matrix based on Pauli measurements. Both ordinary principal component analysis (PCA) and iterative thresholding sparse PCA (ITSPCA) estimators

On energy tests of normality J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201117
Tamás F. Móri, Gábor J. Székely, Maria L. RizzoThe energy test of multivariate normality is an affine invariant test based on a characterization of equal distributions by energy distance. The test statistic is a degenerate kernel Vstatistic, which asymptotically has a sampling distribution that is a Gaussian quadratic form under the null hypothesis of normality. The parameters of the limit distribution are the eigenvalues of the integral operator

Sequential online subsampling for thinning experimental designs J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201105
Luc Pronzato, HaiYing WangWe consider a design problem where experimental conditions (design points Xi) are presented in the form of a sequence of i.i.d. random variables, generated with an unknown probability measure μ, and only a given proportion α∈(0,1) can be selected. The objective is to select good candidates Xi on the fly and maximize a concave function Φ of the corresponding information matrix. The optimal solution

A Bayesian analysis of the matching problem J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201111
Ignacio Vidal, Mário de CastroThe matching problem is known since the beginning of the eighteenth century and Bayesian solutions have been proposed. In this paper, we present a Bayesian analysis of an experiment that also leads to the matching problem. Since in this paper we consider the order in which assignments are made and not only the number of matches, our approach is different from the literature on this problem. Our approach

Generalized principal component analysis for moderately nonstationary vector time series J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201112
Fayed Alshammri, Jiazhu PanThis paper extends the principal component analysis (PCA) to moderately nonstationary vector time series. We propose a method that searches for a linear transformation of the original series such that the transformed series is segmented into uncorrelated subseries with lower dimensions. A columns’ rearrangement method is proposed to regroup transformed series based on their relationships. We discuss

Generalization performance of Lagrangian support vector machine based on Markov sampling J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201121
JingJing Zeng, Yuze Duan, Desheng Wang, Bin Zou, Yue Yin, Jie XuIn this paper, we first establish the generalization bounds of Lagrangian Support Vector Machines (LSVM) based on uniformly ergodic Markov chain (u.e.M.c.) samples. As an application, we also obtain the generalization bounds of LSVM based on strongly mixing sequence, independent and identically distributed (i.i.d.) samples, respectively. The fast learning rates of LSVM for u.e.M.c., strongly mixing

Semiparametric estimation for average causal effects using propensity scorebased spline J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201112
Peng Wu, Xinyi Xu, Xingwei Tong, Qing Jiang, Bo LuWhen estimating the average causal effect in observational studies, researchers have to tackle both selfselection of treatment and outcome modeling. This is difficult because the parametric form of the outcome model is often unknown and there exists a large number of covariates. In this work, we present a semiparametric strategy for estimating the average causal effect by regressing on the propensity

The Hyvärinen scoring rule in Gaussian linear time series models J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201101
Silvia Columbu, Valentina Mameli, Monica Musio, Philip DawidIn this work we study stationary linear timeseries models, and construct and analyse “scorematching” estimators based on the Hyvärinen scoring rule. We consider two scenarios: a single series of increasing length, and an increasing number of independent series of fixed length. In the latter case there are two variants, one based on the full data, and another based on a sufficient statistic. We study

A simultaneous test of mean vector and covariance matrix in highdimensional settings J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201101
Mingxiang Cao, Peng Sun, Junyong ParkIn this paper, the problem of simultaneously testing mean vector and covariance matrix of onesample population is investigated in highdimensional settings. We propose a new test statistic and obtain its asymptotic distributions under null and local alternative hypotheses, respectively. Our asymptotic result for proposed test does not need some conditions such as linearity between the sample size

Minimax estimation of a restricted mean for a oneparameter exponential family J. Stat. Plann. Inference (IF 0.679) Pub Date : 20201102
Éric Marchand, Fanny Rancourt, William E. StrawdermanFor oneparameter exponential families, we provide a unified minimax result for estimating the mean under weighted squared error losses in the presence of a lowerbound restriction. The finding recovers cases for which the result is known, as well as others which are new such as for a negative binomial model. We also study a related selfminimaxity property, obtaining several nonminimax results. Finally