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  • Nonparametric multiple regression estimation for circular response
    Test (IF 1.205) Pub Date : 2020-10-12
    Andrea Meilán-Vila, Mario Francisco-Fernández, Rosa M. Crujeiras, Agnese Panzera

    Nonparametric estimators of a regression function with circular response and \({\mathbb {R}}^d\)-valued predictor are considered in this work. Local polynomial estimators are proposed and studied. Expressions for the asymptotic conditional bias and variance of these estimators are derived, and some guidelines to select asymptotically optimal local bandwidth matrices are also provided. The finite sample

    更新日期:2020-10-12
  • A notion of depth for sparse functional data
    Test (IF 1.205) Pub Date : 2020-09-18
    Carlo Sguera, Sara López-Pintado

    Data depth is a well-known and useful nonparametric tool for analyzing functional data. It provides a novel way of ranking a sample of curves from the center outwards and defining robust statistics, such as the median or trimmed means. It has also been used as a building block for functional outlier detection methods and classification. Several notions of depth for functional data were introduced in

    更新日期:2020-09-20
  • Testing serial independence with functional data
    Test (IF 1.205) Pub Date : 2020-09-10
    Zdeněk Hlávka, Marie Hušková, Simos G. Meintanis

    We consider tests of serial independence for a sequence of functional observations. The new methods are formulated as L2-type criteria based on empirical characteristic functions and are convenient from the computational point of view. We derive asymptotic normality of the proposed test statistics for both discretely and continuously observed functions. In a Monte Carlo study, we show that the new

    更新日期:2020-09-10
  • Multivariate functional data modeling with time-varying clustering
    Test (IF 1.205) Pub Date : 2020-09-09
    Philip A. White, Alan E. Gelfand

    We consider the setting of multivariate functional data collected over time at each of a set of sites. Our objective is to implement model-based clustering of the functions across the sites where we allow such clustering to vary over time. Anticipating dependence between the functions within a site as well as across sites, we model the collection of functions using a multivariate Gaussian process.

    更新日期:2020-09-10
  • Empirical likelihood inference for generalized additive partially linear models
    Test (IF 1.205) Pub Date : 2020-09-05
    Rong Liu, Yichuan Zhao

    Generalized additive partially linear models enjoy the simplicity of GLMs and the flexibility of GAMs because they combine both parametric and nonparametric components. Based on spline-backfitted kernel estimator, we propose empirical likelihood (EL)-based pointwise confidence intervals and simultaneous confidence bands (SCBs) for the nonparametric component functions to make statistical inference

    更新日期:2020-09-07
  • Functional marked point processes: a natural structure to unify spatio-temporal frameworks and to analyse dependent functional data
    Test (IF 1.205) Pub Date : 2020-08-25
    Mohammad Ghorbani, Ottmar Cronie, Jorge Mateu, Jun Yu

    This paper treats functional marked point processes (FMPPs), which are defined as marked point processes where the marks are random elements in some (Polish) function space. Such marks may represent, for example, spatial paths or functions of time. To be able to consider, for example, multivariate FMPPs, we also attach an additional, Euclidean, mark to each point. We indicate how the FMPP framework

    更新日期:2020-08-25
  • Smooth estimation of size distributions in an oriented cylinder model
    Test (IF 1.205) Pub Date : 2020-08-14
    Geurt Jongbloed, Kimberly S. McGarrity, Jilt Sietsma

    Kernel estimators are proposed for estimating the cumulative distribution functions and the probability density functions of several quantities of interest in a stereological oriented cylinder model. This oriented cylinder model was developed to represent anisotropic microstructural features in materials. The asymptotic properties of these estimators are studied, and the estimators are applied to two

    更新日期:2020-08-14
  • 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

    更新日期:2020-07-30
  • 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

    更新日期:2020-07-24
  • 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

    更新日期:2020-07-22
  • 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

    更新日期:2020-07-09
  • 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

    更新日期:2020-07-05
  • 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

    更新日期:2020-07-05
  • 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

    更新日期:2020-06-24
  • 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

    更新日期:2020-06-23
  • 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

    更新日期:2020-06-12
  • 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

    更新日期:2020-06-03
  • 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

    更新日期:2020-05-29
  • 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

    更新日期:2020-05-14
  • 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

    更新日期:2020-04-23
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-20
  • 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

    更新日期:2020-04-18
  • 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”

    更新日期:2020-04-18
  • 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

    更新日期:2020-04-18
  • 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

    更新日期:2020-04-18
  • 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

    更新日期:2020-04-18
  • 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

    更新日期:2020-04-18
  • 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

    更新日期:2020-04-18
  • 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

    更新日期:2020-01-02
  • 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

    更新日期:2020-01-02
  • Deville and Särndal’s calibration: revisiting a 25-years-old successful optimization problem
    Test (IF 1.205) Pub Date : 2019-11-22
    Denis Devaud; Yves Tillé

    In 1992, in a famous paper, Deville and Särndal proposed the calibration method in order to adjust samples on known population totals. This paper had a very important impact in the theory and practice of survey statistics. In this paper, we propose a rigorous formalization of the calibration problem viewed as an optimization problem. We examine the main calibration functions and we discuss the question

    更新日期:2019-11-22
  • Comments on: Deville and Särndal’s calibration: revisiting a 25 years old successful optimization problem
    Test (IF 1.205) Pub Date : 2019-11-22
    Changbao Wu; Shixiao Zhang

    We provide a brief discussion on the development of model calibration techniques and optimal calibration estimation in survey sampling and its relation to Deville and Särndal’s calibration, and applications of model calibration to missing data problems for robust inference.

    更新日期:2019-11-22
  • Robust doubly protected estimators for quantiles with missing data
    Test (IF 1.205) Pub Date : 2019-11-21
    Mariela Sued; Marina Valdora; Víctor Yohai

    Doubly protected methods are widely used for estimating the population mean of an outcome Y from a sample where the response is missing in some individuals. To compensate for the missing responses, a vector \(\mathbf {X}\) of covariates is observed at each individual, and the missing mechanism is assumed to be independent of the response, conditioned on \(\mathbf {X}\) (missing at random). In recent

    更新日期:2019-11-21
  • Small area estimation of proportions under area-level compositional mixed models
    Test (IF 1.205) Pub Date : 2019-11-07
    María Dolores Esteban; María José Lombardía; Esther López-Vizcaíno; Domingo Morales; Agustín Pérez

    This paper introduces area-level compositional mixed models by applying transformations to a multivariate Fay–Herriot model. Small area estimators of the proportions of the categories of a classification variable are derived from the new model, and the corresponding mean squared errors are estimated by parametric bootstrap. Several simulation experiments designed to analyse the behaviour of the introduced

    更新日期:2019-11-07
  • Goodness-of-fit test for a parametric survival function with cure fraction
    Test (IF 1.205) Pub Date : 2019-10-23
    Candida Geerdens; Paul Janssen; Ingrid Van Keilegom

    We consider the survival function for univariate right-censored event time data, when a cure fraction is present. This means that the population consists of two parts: the cured or non-susceptible group, who will never experience the event of interest versus the non-cured or susceptible group, who will undergo the event of interest when followed up sufficiently long. When modeling the data, a parametric

    更新日期:2019-10-23
  • Approximate Bayesian inference for mixture cure models
    Test (IF 1.205) Pub Date : 2019-09-25
    E. Lázaro; C. Armero; V. Gómez-Rubio

    Cure models in survival analysis deal with populations in which a part of the individuals cannot experience the event of interest. Mixture cure models consider the target population as a mixture of susceptible and non-susceptible individuals. The statistical analysis of these models focuses on examining the probability of cure (incidence model) and inferring on the time to event in the susceptible

    更新日期:2019-09-25
  • A goodness-of-fit test for regression models with spatially correlated errors
    Test (IF 1.205) Pub Date : 2019-09-18
    Andrea Meilán-Vila; Jean D. Opsomer; Mario Francisco-Fernández; Rosa M. Crujeiras

    The problem of assessing a parametric regression model in the presence of spatial correlation is addressed in this work. For that purpose, a goodness-of-fit test based on a \(L_2\)-distance comparing a parametric and nonparametric regression estimators is proposed. Asymptotic properties of the test statistic, both under the null hypothesis and under local alternatives, are derived. Additionally, a

    更新日期:2019-09-18
  • Reduced bias nonparametric lifetime density and hazard estimation.
    Test (IF 1.205) Pub Date : 2019-08-17
    Arthur Berg,Dimitris Politis,Kagba Suaray,Hui Zeng

    Kernel-based nonparametric hazard rate estimation is considered with a special class of infinite-order kernels that achieves favorable bias and mean square error properties. A fully automatic and adaptive implementation of a density and hazard rate estimator is proposed for randomly right censored data. Careful selection of the bandwidth in the proposed estimators yields estimates that are more efficient

    更新日期:2019-08-17
  • Goodness-of-fit tests for parametric specifications of conditionally heteroscedastic models
    Test (IF 1.205) Pub Date : 2019-08-12
    M. Dolores Jiménez-Gamero; Sangyeol Lee; Simos G. Meintanis

    We consider a goodness-of-fit test for certain parametrizations of conditionally heteroscedastic time series with unobserved components. Our test is quite general in that it can be employed to validate any given specification of arbitrary order and may even be invoked for testing not just GARCH models but also some related models such as autoregressive conditional duration models. The test statistic

    更新日期:2019-08-12
  • A class of asymptotically efficient estimators based on sample spacings
    Test (IF 1.205) Pub Date : 2019-07-29
    M. Ekström; S. M. Mirakhmedov; S. Rao Jammalamadaka

    In this paper, we consider general classes of estimators based on higher-order sample spacings, called the Generalized Spacings Estimators. Such classes of estimators are obtained by minimizing the Csiszár divergence between the empirical and true distributions for various convex functions, include the “maximum spacing estimators” as well as the maximum likelihood estimators (MLEs) as special cases

    更新日期:2019-07-29
  • Analysis of correlated Birnbaum–Saunders data based on estimating equations
    Test (IF 1.205) Pub Date : 2019-07-20
    Aline B. Tsuyuguchi; Gilberto A. Paula; Michelli Barros

    Estimating equations for analyzing correlated Birnbaum–Saunders (BS) data are derived in this paper. A regression model is proposed for modeling the median of the life time until the failure, and a reweighted iterative process is developed for the joint estimation of the regression coefficients and the shape and correlation parameters. Diagnostic procedures, such as residual analysis and sensitivity

    更新日期:2019-07-20
  • Compositional data: the sample space and its structure
    Test (IF 1.205) Pub Date : 2019-07-16
    Juan José Egozcue; Vera Pawlowsky-Glahn

    The log-ratio approach to compositional data (CoDa) analysis has now entered a mature phase. The principles and statistical tools introduced by J. Aitchison in the eighties have proven successful in solving a number of applied problems. The algebraic–geometric structure of the sample space, tailored to those principles, was developed at the beginning of the millennium. Two main ideas completed the

    更新日期:2019-07-16
  • Supervised classification of geometrical objects by integrating currents and functional data analysis
    Test (IF 1.205) Pub Date : 2019-07-09
    S. Barahona; P. Centella; X. Gual-Arnau; M. V. Ibáñez; A. Simó

    This paper focuses on the application of supervised classification techniques to a set of geometrical objects (bodies) characterized by currents, in particular, discriminant analysis and some nonparametric methods. A current is a relevant mathematical object to model geometrical data, like hypersurfaces, through integration of vector fields over them. As a consequence of the choice of a vector-valued

    更新日期:2019-07-09
  • 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

    更新日期:2019-07-04
  • 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

    更新日期:2019-07-03
  • 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

    更新日期:2019-06-24
  • 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

    更新日期:2019-06-15
  • 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

    更新日期:2019-05-22
  • 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

    更新日期:2019-05-11
  • 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

    更新日期:2019-05-09
  • 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

    更新日期:2019-05-07
  • 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

    更新日期:2019-05-03
  • 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 \({\mathcal {G}}^0\)

    更新日期:2019-04-29
  • 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

    更新日期:2019-04-20
  • 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

    更新日期:2019-04-20
  • 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.

    更新日期:2019-04-16
  • Data science, big data and statistics
    Test (IF 1.205) Pub Date : 2019-04-08
    Pedro Galeano; Daniel Peña

    This article analyzes how Big Data is changing the way we learn from observations. We describe the changes in statistical methods in seven areas that have been shaped by the Big Data-rich environment: the emergence of new sources of information; visualization in high dimensions; multiple testing problems; analysis of heterogeneity; automatic model selection; estimation methods for sparse models; and

    更新日期:2019-04-08
  • Comments on: Data science, big data and statistics
    Test (IF 1.205) Pub Date : 2019-04-08
    Peter Bühlmann

    We congratulate Pedro Galeano and Daniel Peña for a nice paper on the emerging theme of data science and the role of statistics.

    更新日期:2019-04-08
  • 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

    更新日期:2019-03-30
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