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  • A robust proposal of estimation for the sufficient dimension reduction problem
    Test (IF 1.205) Pub Date : 2021-01-03
    Andrea Bergesio, María Eugenia Szretter Noste, Víctor J. Yohai

    In nonparametric regression contexts, when the number of covariables is large, we face the curse of dimensionality. One way to deal with this problem when the sample is not large enough is using a reduced number of linear combinations of the explanatory variables that contain most of the information about the response variable. This leads to the so-called sufficient reduction problem. The purpose of

    更新日期:2021-01-03
  • Goodness of fit for models with intractable likelihood
    Test (IF 1.205) Pub Date : 2021-01-02
    Stefano Cabras, María Eugenia Castellanos, Oliver Ratmann

    Routine goodness-of-fit analyses of complex models with intractable likelihoods are hampered by a lack of computationally tractable diagnostic measures with well-understood frequency properties, that is, with a known sampling distribution. This frustrates the ability to assess the extremity of the data relative to fitted simulation models in terms of pre-specified test statistics, an essential requirement

    更新日期:2021-01-03
  • Covariate-adjusted multiple testing in genome-wide association studies via factorial hidden Markov models
    Test (IF 1.205) Pub Date : 2021-01-02
    Tingting Cui, Pengfei Wang, Wensheng Zhu

    It is more and more important to consider the dependence structure among multiple testings, especially for the genome-wide association studies (GWAS). The existing procedures, such as local index of significance (LIS) and pooled local index of significance (PLIS), were proposed to test hidden Markov model (HMM)-dependent hypotheses under the framework of compound decision theory, which was successfully

    更新日期:2021-01-02
  • Correction to: Probability of default estimation in credit risk using a nonparametric approach
    Test (IF 1.205) Pub Date : 2020-12-18
    Rebeca Peláez Suárez, Ricardo Cao Abad, Juan M. Vilar Fernández

    The authors would like to correct the errors in the publication of the original article.

    更新日期:2020-12-18
  • Comments on: Tests for multivariate normality—a critical review with emphasis on weighted $$L^2$$ L 2 -statistics
    Test (IF 1.205) Pub Date : 2020-12-01
    Simos G. Meintanis

    We discuss extension of the BHEP test to more general families of distributions.

    更新日期:2020-12-01
  • Tests for multivariate normality—a critical review with emphasis on weighted $$L^2$$ L 2 -statistics
    Test (IF 1.205) Pub Date : 2020-12-01
    Bruno Ebner, Norbert Henze

    This article gives a synopsis on new developments in affine invariant tests for multivariate normality in an i.i.d.-setting, with special emphasis on asymptotic properties of several classes of weighted \(L^2\)-statistics. Since weighted \(L^2\)-statistics typically have limit normal distributions under fixed alternatives to normality, they open ground for a neighborhood of model validation for normality

    更新日期:2020-12-01
  • A likelihood-based approach for cure regression models
    Test (IF 1.205) Pub Date : 2020-11-23
    Kevin Burke, Valentin Patilea

    We propose a new likelihood-based approach for estimation, inference and variable selection for parametric cure regression models in time-to-event analysis under random right-censoring. In this context, it often happens that some subjects are “cured”, i.e., they will never experience the event of interest. Then, the sample of censored observations is an unlabeled mixture of cured and “susceptible”

    更新日期:2020-11-25
  • Simple measures of uncertainty for model selection
    Test (IF 1.205) Pub Date : 2020-11-01
    Xiaohui Liu, Yuanyuan Li, Jiming Jiang

    We develop two simple measures of uncertainty for a model selection procedure. The first measure is similar in spirit to confidence set in parameter estimation; the second measure is focusing on error in model selection. The proposed methods are simpler, both conceptually and computationally, than the existing measures of uncertainty in model selection. We recognize major differences between model

    更新日期:2020-11-02
  • 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
  • Automated learning of mixtures of factor analysis models with missing information
    Test (IF 1.205) Pub Date : 2020-01-29
    Wan-Lun Wang, Tsung-I Lin

    The mixture of factor analyzers (MFA) model has emerged as a useful tool to perform dimensionality reduction and model-based clustering for heterogeneous data. In seeking the most appropriate number of factors (q) of a MFA model with the number of components (g) fixed a priori, a two-stage procedure is commonly implemented by firstly carrying out parameter estimation over a set of prespecified numbers

    更新日期:2020-01-29
  • Kumaraswamy regression model with Aranda-Ordaz link function
    Test (IF 1.205) Pub Date : 2020-01-23
    Guilherme Pumi, Cristine Rauber, Fábio M. Bayer

    In this work, we introduce a regression model for double-bounded variables in the interval (0, 1) following a Kumaraswamy distribution. The model resembles a generalized linear model, in which the response’s median is modeled by a regression structure through the asymmetric Aranda-Ordaz parametric link function. We consider the maximum likelihood approach to estimate the regression and the link function

    更新日期:2020-01-23
  • Maximum observed likelihood prediction of future record values
    Test (IF 1.205) Pub Date : 2020-01-22
    Grigoriy Volovskiy, Udo Kamps

    Point prediction of future upper record values is considered. For an underlying absolutely continuous distribution with strictly increasing cumulative distribution function, the general form of the predictor obtained by maximizing the observed predictive likelihood function is established. The results are illustrated for the exponential, extreme-value and power-function distributions, and the performance

    更新日期:2020-01-22
  • Implementation of compound optimal design strategy in censored life-testing experiment
    Test (IF 1.205) Pub Date : 2020-01-08
    Ritwik Bhattacharya

    Single-objective optimal designs or constraint optimal designs have widely been studied in life-testing experiment literature. However, the experiments having multiple objectives did not get relevant attention so far. Compound optimal designs are usually employed in statistical design problems where the experiment possesses multiple goals. This article introduces the concept of compound optimal design

    更新日期:2020-01-08
  • 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
  • The nonparametric location-scale mixture cure model
    Test (IF 1.205) Pub Date : 2019-12-17
    Justin Chown, Cédric Heuchenne, Ingrid Van Keilegom

    We propose completely nonparametric methodology to investigate location-scale modeling of two-component mixture cure models that is similar in spirit to accelerated failure time models, where the responses of interest are only indirectly observable due to the presence of censoring and the presence of long-term survivors that are always censored. We use nonparametric estimators of the location-scale

    更新日期:2019-12-17
  • Robust model-based clustering with mild and gross outliers
    Test (IF 1.205) Pub Date : 2019-11-28
    Alessio Farcomeni, Antonio Punzo

    We propose a model-based clustering procedure where each component can take into account cluster-specific mild outliers through a flexible distributional assumption, and a proportion of observations is additionally trimmed. We propose a penalized likelihood approach for estimation and selection of the proportions of mild and gross outliers. A theoretically grounded penalty parameter is then obtained

    更新日期:2019-11-28
  • Robust estimation and inference for general varying coefficient models with missing observations
    Test (IF 1.205) Pub Date : 2019-11-27
    Francesco Bravo

    This paper considers estimation and inference for a class of varying coefficient models in which some of the responses and some of the covariates are missing at random and outliers are present. The paper proposes two general estimators—and a computationally attractive and asymptotically equivalent one-step version of them—that combine inverse probability weighting and robust local linear estimation

    更新日期:2019-11-27
  • 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
  • On approximate validation of models: a Kolmogorov–Smirnov-based approach
    Test (IF 1.205) Pub Date : 2019-11-19
    E. del Barrio, H. Inouzhe, C. Matrán

    Classical tests of fit typically reject a model for large enough real data samples. In contrast, often in statistical practice, a model offers a good description of the data even though it is not the ‘true’ random generator. We consider a more flexible approach based on contamination neighbourhoods: using trimming methods and the Kolmogorov metric, we introduce a functional statistic measuring departures

    更新日期:2019-11-19
  • On semi-supervised learning
    Test (IF 1.205) Pub Date : 2019-11-16
    A. Cholaquidis, R. Fraiman, M. Sued

    Major efforts have been made, mostly in the machine learning literature, to construct good predictors combining unlabelled and labelled data. These methods are known as semi-supervised. They deal with the problem of how to take advantage, if possible, of a huge amount of unlabelled data to perform classification in situations where there are few labelled data. This is not always feasible: it depends

    更新日期:2019-11-16
  • 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
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