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  • Sparse semiparametric regression when predictors are mixture of functional and high-dimensional variables
    Test (IF 1.205) Pub Date : 2020-07-30
    Silvia Novo, Germán Aneiros, Philippe Vieu

    This paper aims to front with dimensionality reduction in regression setting when the predictors are a mixture of functional variable and high-dimensional vector. A flexible model, combining both sparse linear ideas together with semiparametrics, is proposed. A wide scope of asymptotic results is provided: this covers as well rates of convergence of the estimators as asymptotic behaviour of the variable

  • Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models
    Test (IF 1.205) Pub Date : 2020-07-24
    Alessio Farcomeni, Monia Ranalli, Sara Viviani

    We present a method for dimension reduction of multivariate longitudinal data, where new variables are assumed to follow a latent Markov model. New variables are obtained as linear combinations of the multivariate outcome as usual. Weights of each linear combination maximize a measure of separation of the latent intercepts, subject to orthogonality constraints. We evaluate our proposal in a simulation

  • On a class of repulsive mixture models
    Test (IF 1.205) Pub Date : 2020-07-22
    José J. Quinlan, Fernando A. Quintana, Garritt L. Page

    Finite or infinite mixture models are routinely used in Bayesian statistical practice for tasks such as clustering or density estimation. Such models are very attractive due to their flexibility and tractability. However, a common problem in fitting these or other discrete models to data is that they tend to produce a large number of overlapping clusters. Some attention has been given in the statistical

  • Semiparametric mixture regression with unspecified error distributions
    Test (IF 1.205) Pub Date : 2020-07-08
    Yanyuan Ma, Shaoli Wang, Lin Xu, Weixin Yao

    In fitting a mixture of linear regression models, normal assumption is traditionally used to model the error and then regression parameters are estimated by the maximum likelihood estimators (MLE). This procedure is not valid if the normal assumption is violated. By extending the semiparametric regression estimator proposed by Hunter and Young (J Nonparametr Stat 24:19–38, 2012a) which requires the

  • Maximum likelihood estimators based on discrete component lifetimes of a k -out-of- n system
    Test (IF 1.205) Pub Date : 2020-07-05
    Anna Dembińska, Krzysztof Jasiński

    This paper deals with parametric inference about the independent and identically distributed discrete lifetimes of components of a k-out-of-n system. We consider the maximum likelihood estimation assuming that the available data consists of component failure times observed up to and including the moment of the breakdown of the system. First, we provide general conditions for the almost sure existence

  • Probability of default estimation in credit risk using a nonparametric approach
    Test (IF 1.205) Pub Date : 2020-07-04
    Rebeca Peláez Suárez, Ricardo Cao Abad, Juan M. Vilar Fernández

    In this paper, four nonparametric estimators of the probability of default in credit risk are proposed and compared. They are derived from estimators of the conditional survival function for censored data. Asymptotic expressions for the bias and the variance of these probability of default estimators are derived from similar properties for the conditional survival function estimators. A simulation

  • Modelling informative time points: an evolutionary process approach
    Test (IF 1.205) Pub Date : 2020-06-24
    Andreia Monteiro, Raquel Menezes, Maria Eduarda Silva

    Real time series sometimes exhibit various types of “irregularities”: missing observations, observations collected not regularly over time for practical reasons, observation times driven by the series itself, or outlying observations. However, the vast majority of methods of time series analysis are designed for regular time series only. A particular case of irregularly spaced time series is that in

  • Bootstrapping regression models with locally stationary disturbances
    Test (IF 1.205) Pub Date : 2020-06-19
    Guillermo Ferreira, Jorge Mateu, Jose A. Vilar, Joel Muñoz

    A linear regression model with errors following a time-varying process is considered. In this class of models, the smoothness condition both in the trend function and in the correlation structure of the error term ensures that these models can be locally approximated by stationary processes, leading to a general class of linear regression models with locally stationary errors. We focus here on the

  • Second-order and local characteristics of network intensity functions
    Test (IF 1.205) Pub Date : 2020-06-12
    Matthias Eckardt, Jorge Mateu

    The last decade has witnessed an increase of interest in the spatial analysis of structured point patterns over networks whose analysis is challenging because of geometrical complexities and unique methodological problems. In this context, it is essential to incorporate the network specificity into the analysis as the locations of events are restricted to areas covered by line segments. Relying on

  • Testing for the sandwich-form covariance matrix of the quasi-maximum likelihood estimator
    Test (IF 1.205) Pub Date : 2020-06-03
    Lijuan Huo, Jin Seo Cho

    This study tests for the sandwich-form asymptotic covariance matrices entailed by conditionally heteroskedastic and/or autocorrelated regression errors or conditionally uncorrelated homoskedastic errors. In doing so, we enable the empirical researcher to estimate the asymptotic covariance matrix of the quasi-maximum likelihood estimator by supposing a possibly misspecified model for error distribution

  • WIKS: a general Bayesian nonparametric index for quantifying differences between two populations
    Test (IF 1.205) Pub Date : 2020-05-29
    Rafael de Carvalho Ceregatti, Rafael Izbicki, Luis Ernesto Bueno Salasar

    A key problem in many research investigations is to decide whether two samples have the same distribution. Numerous statistical methods have been devoted to this issue, but only few considered a Bayesian nonparametric approach. In this paper, we propose a novel nonparametric Bayesian index (WIKS) for quantifying the difference between two populations \(P_1\) and \(P_2\), which is defined by a weighted

  • Objective Bayesian model choice for non-nested families: the case of the Poisson and the negative binomial
    Test (IF 1.205) Pub Date : 2020-05-14
    Elías Moreno, Carmen Martínez, Francisco–José Vázquez–Polo

    Selecting a statistical model from a set of competing models is a central issue in the scientific task, and the Bayesian approach to model selection is based on the posterior model distribution, a quantification of the updated uncertainty on the entertained models. We present a Bayesian procedure for choosing a family between the Poisson and the geometric families and prove that the procedure is consistent

  • Inference and computation with generalized additive models and their extensions
    Test (IF 1.205) Pub Date : 2020-04-23
    Simon N. Wood

    Regression models in which a response variable is related to smooth functions of some predictor variables are popular as a result of their appealing balance between flexibility and interpretability. Since the original generalized additive models of Hastie and Tibshirani (Generalized additive models. Chapman & Hall, Boca Raton, 1990) numerous model extensions have been proposed, and a variety of practically

  • Selection model for domains across time: application to labour force survey by economic activities
    Test (IF 1.205) Pub Date : 2020-04-21
    María José Lombardía, Esther López-Vizcaíno, Cristina Rueda

    This paper introduces a small area estimation approach that borrows strength across domains (areas) and time and is efficiently used to obtain labour force estimators by economic activity. Specifically, the data across time are used to select different models for each domain; such selection is done with an aggregated mixed generalized Akaike information criterion statistic which is obtained using data

  • Comparisons of policies based on relevation and replacement by a new one unit in reliability
    Test (IF 1.205) Pub Date : 2020-04-20
    Félix Belzunce, Carolina Martínez-Riquelme, José A. Mercader, José M. Ruiz

    The purpose of this paper is to study the role of the relevation transform, where a failed unit is replaced by a used unit with the same age as the failed one, as an alternative to the policy based on the replacement by a new one. In particular, we compare the stochastic processes arising from a policy based on the replacement of a failed unit by a new one and from the one in which the unit is being

  • Tail dependence and smoothness of time series
    Test (IF 1.205) Pub Date : 2020-04-18
    Helena Ferreira, Marta Ferreira

    The risk of catastrophes is related to the possibility of occurring extreme values. Several statistical methodologies have been developed in order to evaluate the propensity of a process for the occurrence of high values and the permanence of these in time. The extremal index \(\theta \) (Leadbetter in Z Wahrscheinlichkeitstheor Verw Geb 65:291–306, 1983) allows to infer the tendency for clustering

  • Accounting for dependent informative sampling in model-based finite population inference
    Test (IF 1.205) Pub Date : 2020-04-01
    Isabel Molina, Malay Ghosh

    The paper considers model-based inference for finite population parameters under informative sampling, when the draws of the different units are not independent and the joint selection probability is modeled using a copula. We extend the “sample likelihood” approach to the case of dependent draws and provide the expression of the likelihood given the selected sample, called here “selection likelihood”

  • Modeling dependence via copula of functionals of Fourier coefficients
    Test (IF 1.205) Pub Date : 2020-03-13
    Charles Fontaine, Ron D. Frostig, Hernando Ombao

    The goal of this paper is to develop a measure for characterizing complex dependence between time series that cannot be captured by traditional measures such as correlation and coherence. Our approach is to use copula models of functionals of the Fourier coefficients which is a generalization of coherence. Here, we use standard parametric copula models with a single parameter from both elliptical and

  • Entropy-based pivotal statistics for multi-sample problems in planar shape
    Test (IF 1.205) Pub Date : 2020-03-10
    W. V. Félix de Lima, A. D. C. Nascimento, G. J. A. Amaral

    Morphometric data come from several natural and man-made phenomena; e.g., biological processes and medical image processing. The analysis of these data—known as statistical shape analysis (SSA)—requires tailored methods because the majority of multivariate techniques are for the Euclidean space. An important branch at the SSA consists in using landmark data in two dimensions, called planar shape. Hypothesis

  • Parameter estimation and diagnostic tests for INMA(1) processes
    Test (IF 1.205) Pub Date : 2019-03-30
    Boris Aleksandrov, Christian H. Weiß

    The INMA(1) model, an integer-valued counterpart to the usual moving-average model of order 1, gained recently importance for insurance applications. After a comprehensive discussion of stochastic properties of the INMA(1) model, we develop diagnostic tests regarding the marginal distribution (overdispersion, zero inflation) and the autocorrelation structure. We also derive formulae for correcting

  • A Fay–Herriot model when auxiliary variables are measured with error
    Test (IF 1.205) Pub Date : 2019-03-22
    Jan Pablo Burgard, María Dolores Esteban, Domingo Morales, Agustín Pérez

    The Fay–Herriot model is an area-level linear mixed model that is widely used for estimating the domain means of a given target variable. Under this model, the dependent variable is a direct estimator calculated by using the survey data and the auxiliary variables are true domain means obtained from external data sources. Administrative registers do not always give good auxiliary variables so that

  • Comparisons of coherent systems under the time-transformed exponential model
    Test (IF 1.205) Pub Date : 2019-04-20
    Jorge Navarro, Julio Mulero

    The coherent systems are basic concepts in reliability theory and survival analysis. They contain as particular cases the popular series, parallel and k-out-of-n systems (order statistics). Many results have been obtained for them by assuming that the component lifetimes are independent. In many practical cases, this assumption is unrealistic. In this paper, we study them by assuming a time-transformed

  • Testing normality via a distributional fixed point property in the Stein characterization
    Test (IF 1.205) Pub Date : 2019-02-22
    Steffen Betsch, Bruno Ebner

    We propose two families of tests for the classical goodness-of-fit problem to univariate normality. The new procedures are based on \(L^2\)-distances of the empirical zero-bias transformation to the empirical distribution or the normal distribution function. Weak convergence results are derived under the null hypothesis, under contiguous as well as under fixed alternatives. A comparative finite-sample

  • On active learning methods for manifold data
    Test (IF 1.205) Pub Date : 2020-01-02
    Hang Li, Enrique Del Castillo, George Runger

    Active learning is a major area of interest within the field of machine learning, especially when the labeled instances are very difficult, time-consuming or expensive to obtain. In this paper, we review various active learning methods for manifold data, where the intrinsic manifold structure of data is also incorporated into the active learning query strategies. In addition, we present a new manifold-based

  • Optimal designs in multiple group random coefficient regression models
    Test (IF 1.205) Pub Date : 2019-04-16
    Maryna Prus

    The subject of this work is multiple group random coefficients regression models with several treatments and one control group. Such models are often used for studies with cluster randomized trials. We investigate A-, D- and E-optimal designs for estimation and prediction of fixed and random treatment effects, respectively, and illustrate the obtained results by numerical examples.

  • Is perfect repair always perfect?
    Test (IF 1.205) Pub Date : 2019-02-18
    Ji Hwan Cha, Maxim Finkelstein

    Most often, perfect repair is conventionally understood as a replacement of the failed item by the new one. However, contrary to the common perception, new does not mean automatically that the distribution to the next failure is identical to that on the previous cycle. First, it can be different due to dynamic environment and, secondly, due to heterogeneity of items for replacement. Both of these causes

  • Rejoinder on: “On active learning methods for manifold data”
    Test (IF 1.205) Pub Date : 2020-01-02
    Hang Li, Enrique Del Castillo, George Runger

    We thank the discussants for their comments and careful reading of our manuscript, which have enhanced and complemented our presentation. We also thank the editors of TEST for this opportunity to clarify some aspects of our work in more detail. In what follows, we first address some points touched by both sets of discussants, and then consider comments made individually by each of them. We conclude

  • Robust estimators in a generalized partly linear regression model under monotony constraints
    Test (IF 1.205) Pub Date : 2019-02-13
    Graciela Boente, Daniela Rodriguez, Pablo Vena

    In this paper, we consider the situation in which the observations follow an isotonic generalized partly linear model. Under this model, the mean of the responses is modelled, through a link function, linearly on some covariates and nonparametrically on an univariate regressor in such a way that the nonparametric component is assumed to be a monotone function. A class of robust estimates for the monotone

  • Objective Bayesian comparison of order-constrained models in contingency tables
    Test (IF 1.205) Pub Date : 2019-03-22
    Roberta Paroli, Guido Consonni

    In social and biomedical sciences, testing in contingency tables often involves order restrictions on cell probabilities parameters. We develop objective Bayes methods for order-constrained testing and model comparison when observations arise under product binomial or multinomial sampling. Specifically, we consider tests for monotone order of the parameters against equality of all parameters. Our strategy

  • Oracally efficient estimation for dense functional data with holiday effects
    Test (IF 1.205) Pub Date : 2019-04-20
    Li Cai, Lisha Li, Simin Huang, Liang Ma, Lijian Yang

    Existing functional data analysis literature has mostly overlooked data with spikes in mean, such as weekly sporting goods sales by a salesperson which spikes around holidays. For such functional data, two-step estimation procedures are formulated for the population mean function and holiday effect parameters, which correspond to the population sales curve and the spikes in sales during holiday times

  • Fully and empirical Bayes approaches to estimating copula-based models for bivariate mixed outcomes using Hamiltonian Monte Carlo
    Test (IF 1.205) Pub Date : 2020-02-25
    Elizabeth D. Schifano, Himchan Jeong, Ved Deshpande, Dipak K. Dey

    We provide a fully Bayesian approach to conduct estimation and inference for a copula model to jointly analyze bivariate mixed outcomes. To obtain posterior samples, we use Hamiltonian Monte Carlo, which avoids the random walk behavior of Metropolis and Gibbs sampling algorithms. We also provide an empirical Bayes approach to estimate the copula parameter, which is useful when prior specification on

  • On the prevalence of information inconsistency in normal linear models
    Test (IF 1.205) Pub Date : 2020-02-20
    Joris Mulder, James O. Berger, Víctor Peña, M. J. Bayarri

    Informally, ‘information inconsistency’ is the property that has been observed in some Bayesian hypothesis testing and model selection scenarios whereby the Bayesian conclusion does not become definitive when the data seem to become definitive. An example is that, when performing a t test using standard conjugate priors, the Bayes factor of the alternative hypothesis to the null hypothesis remains

  • On the concept of B -statistical uniform integrability of weighted sums of random variables and the law of large numbers with mean convergence in the statistical sense
    Test (IF 1.205) Pub Date : 2020-02-18
    Manuel Ordóñez Cabrera, Andrew Rosalsky, Mehmet Ünver, Andrei Volodin

    In this correspondence, for a nonnegative regular summability matrix B and an array \(\left\{ a_{nk}\right\} \) of real numbers, the concept of B-statistical uniform integrability of a sequence of random variables \(\left\{ X_{k}\right\} \) with respect to \(\left\{ a_{nk}\right\} \) is introduced. This concept is more general and weaker than the concept of \(\left\{ X_{k}\right\} \) being uniformly

  • Goodness-of-fit tests for censored regression based on artificial data points
    Test (IF 1.205) Pub Date : 2019-07-04
    Wenceslao González Manteiga, Cédric Heuchenne, César Sánchez Sellero, Alessandro Beretta

    Suppose we have a location-scale regression model where the location is the conditional mean and the scale is the conditional standard deviation; the response is possibly right-censored, the covariate is fully observed, and the error is independent of the covariate. We propose new goodness-of-fit testing procedures for the conditional mean and variance based on an integrated regression function technique

  • Depth-based weighted jackknife empirical likelihood for non-smooth U -structure equations
    Test (IF 1.205) Pub Date : 2019-07-03
    Yongli Sang, Xin Dang, Yichuan Zhao

    In many applications, parameters of interest are estimated by solving some non-smooth estimating equations with U-statistic structure. Jackknife empirical likelihood (JEL) approach can solve this problem efficiently by reducing the computation complexity of the empirical likelihood (EL) method. However, as EL, JEL suffers the sensitivity problem to outliers. In this paper, we propose a weighted jackknife

  • Locally efficient estimation in generalized partially linear model with measurement error in nonlinear function
    Test (IF 1.205) Pub Date : 2019-06-24
    Qianqian Wang, Yanyuan Ma, Guangren Yang

    We investigate the errors in covariates issues in a generalized partially linear model. Different from the usual literature (Ma and Carroll in J Am Stat Assoc 101:1465–1474, 2006), we consider the case where the measurement error occurs to the covariate that enters the model nonparametrically, while the covariates precisely observed enter the model parametrically. To avoid the deconvolution type operations

  • Residual and influence analysis to a general class of simplex regression
    Test (IF 1.205) Pub Date : 2019-06-15
    Patrícia L. Espinheira, Alisson de Oliveira Silva

    In this paper, we propose a residual and local influence analysis for diagnostics in a general class of simplex regression model. Here, we introduce this class in which the predictors involve covariates and nonlinear functions in the parameters. We provide closed-form expressions for the score functions, information matrices, as well a procedure for the choice of initial guesses to be used in the Fisher’s

  • Fitting spatial max-mixture processes with unknown extremal dependence class: an exploratory analysis tool
    Test (IF 1.205) Pub Date : 2019-05-22
    A. Abu-Awwad, V. Maume-Deschamps, P. Ribereau

    A flexible model called the max-mixture model has been introduced for modeling situations where the extremal dependence structure type may vary with distance. In this paper, we propose a novel estimation procedure for spatial max-mixture model parameters. Our procedure is based on the madogram, a dependence measure used in geostatistics to describe spatial structures. A nonlinear least squares minimization

  • Bayesian sequential design for Copula models
    Test (IF 1.205) Pub Date : 2019-05-11
    S. G. J. Senarathne, C. C. Drovandi, J. M. McGree

    Bayesian design requires determining the value of controllable variables in an experiment to maximise the information that will be obtained for subsequently collected data, with the majority of research in this field being focused on experiments that yield a univariate response. In this paper, a robust and computationally efficient Bayesian design approach is proposed to derive designs for experiments

  • Multi-criteria-based optimal life-testing plans under hybrid censoring scheme
    Test (IF 1.205) Pub Date : 2019-05-09
    Ritwik Bhattacharya, Baidya Nath Saha, Graceila González Farías, Narayanaswamy Balakrishnan

    In designing an optimal life-testing experiment under censoring setup, the design parameters are usually chosen by optimizing a suitable criterion function. The criterion function is chosen by using either a variance-based or a cost-based model, and sometimes a combination of both these factors. However, it is an optimization problem with a single objective function. In this article, a multi-criteria-based

  • Estimators of quantile difference between two samples with length-biased and right-censored data
    Test (IF 1.205) Pub Date : 2019-05-07
    Li Xun, Li Tao, Yong Zhou

    In this paper, the difference between the quantiles of two samples is investigated. One sample comes from a prevalent cohort with a stable incidence rate. Then, the observed survival times are length-biased and right-censored data. Another sample is drawn from an incident cohort study with right-censored data. We estimate the quantile difference based on different estimating equations. That is because

  • Nuisance-parameter-free changepoint detection in non-stationary series
    Test (IF 1.205) Pub Date : 2019-05-03
    Michal Pešta, Martin Wendler

    Many changepoint detection procedures rely on the estimation of nuisance parameters (like long-run variance). If a change has occurred, estimators might be biased and data adaptive rules for the choice of tuning parameters might not work as expected. If the data are not stationary, this becomes more challenging. The aim of this paper is to present two changepoint tests, which involve neither nuisance

  • Comparing samples from the $${\mathcal {G}}^0$$G0 distribution using a geodesic distance
    Test (IF 1.205) Pub Date : 2019-04-29
    Alejandro C. Frery, Juliana Gambini

    The \({\mathcal {G}}^0\) distribution is widely used for monopolarized SAR image modeling because it can characterize regions with different degrees of texture accurately. It is indexed by three parameters: the number of looks (which can be estimated for the whole image), a scale parameter and a texture parameter. This paper presents a new proposal for comparing samples from the \({\mathcal {G}}^0\)

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