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  • Semiparametric analysis of interval‐censored failure time data with outcome‐dependent observation schemes
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-12-21
    Yayuan Zhu; Ziqi Chen; Jerald F. Lawless

    Disease progression is often monitored by intermittent follow‐up “visits” in longitudinal cohort studies, resulting in interval‐censored failure time outcomes. Furthermore, the timing and frequency of visits is often found related to a person's history of disease‐related variables in practice. This article develops a semiparametric estimation approach using weighted binomial regression and a kernel

    更新日期:2021-01-19
  • On estimation in the nested case‐control design under nonproportional hazards
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-12-22
    Michelle M. Nuño; Daniel L. Gillen

    Analysis of time‐to‐event data using Cox's proportional hazards (PH) model is ubiquitous in scientific research. A sample is taken from the population of interest and covariate information is collected on everyone. If the event of interest is rare and covariate information is difficult to collect, the nested case‐control (NCC) design reduces costs with minimal impact on inferential precision. Under

    更新日期:2021-01-19
  • Semiparametric Estimation and Model Selection for Conditional Mixture Copula Models
    Scand. J. Stat. (IF 0.924) Pub Date : 2021-01-17
    Guannan Liu; Wei Long; Bingduo Yang; Zongwu Cai

    Conditional copula models allow the dependence structure among variables to vary with covariates, and thus can describe the evolution of the dependence structure with those factors. This paper proposes a conditional mixture copula which is a weighted average of several individual conditional copulas. We allow both the weights and copula parameters to vary with a covariate so that the conditional mixture

    更新日期:2021-01-18
  • Nonstationary Space‐time Covariance Functions induced by Dynamical Systems
    Scand. J. Stat. (IF 0.924) Pub Date : 2021-01-17
    Rachid Senoussi; Emilio Porcu

    This paper provides a novel approach to nonstationarity by considering a bridge between differential equations and spatial fields. We consider the dynamical transformation of a given spatial process undergoing the action of a temporal flow of space diffeomorphisms. Such dynamical deformations are shown to be connected to certain classes of ordinary and partial differential equations.

    更新日期:2021-01-18
  • A new lack‐of‐fit test for quantile regression with censored data
    Scand. J. Stat. (IF 0.924) Pub Date : 2021-01-10
    Mercedes Conde‐Amboage; Ingrid Van Keilegom; Wenceslao González‐Manteiga

    A new lack‐of‐fit test for quantile regression models will be presented for the case where the response variable is right‐censored. The test is based on the cumulative sum of residuals, and it extends the ideas of He and Zhu (2003) to censored quantile regression. It will be shown that the empirical process associated with the test statistic converges to a Gaussian process under the null hypothesis

    更新日期:2021-01-11
  • Testing for conditional independence: A groupwise dimension reduction‐based adaptive‐to‐model approach
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-11-29
    Xuehu Zhu; Jun Lu; Jun Zhang; Lixing Zhu

    In this article, we propose an adaptive‐to‐model test for conditional independence through groupwise dimension reduction developed in sufficient dimension reduction field. The test statistic under the null hypothesis is asymptotically normally distributed. Although it is also based on nonparametric estimation like any local smoothing tests for conditional independence, its behavior is similar to existing

    更新日期:2021-01-07
  • Fitting inhomogeneous phase‐type distributions to data: the univariate and the multivariate case
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-11-18
    Hansjörg Albrecher; Mogens Bladt; Jorge Yslas

    The class of inhomogeneous phase‐type distributions (IPH) was recently introduced in Albrecher & Bladt (2019) as an extension of the classical phase‐type (PH) distributions. Like PH distributions, the class of IPH is dense in the class of distributions on the positive halfline, but leads to more parsimonious models in the presence of heavy tails. In this paper we propose a fitting procedure for this

    更新日期:2020-12-31
  • Nonparametric estimation of the fragmentation kernel based on a partial differential equation stationary distribution approximation
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-11-20
    Van Ha Hoang; Thanh Mai Pham Ngoc; Vincent Rivoirard; Viet Chi Tran

    We consider a stochastic individual‐based model in continuous time to describe a size‐structured population for cell divisions. This model is motivated by the detection of cellular aging in biology. We here address the problem of nonparametric estimation of the kernel ruling the divisions based on the eigenvalue problem related to the asymptotic behavior in large population. This inverse problem involves

    更新日期:2020-12-24
  • Detecting early or late changes in linear models with heteroscedastic errors
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-11-29
    Lajos Horváth; Curtis Miller; Gregory Rice

    We construct and study a test to detect possible change points in the regression parameters of a linear model when the model errors and covariates may exhibit heteroscedasticity. Being based on a new trimming scheme for the CUSUM process introduced in Horváth et al. (2020), this test is particularly well suited to detect changes that might occur near the endpoints of the sample. A complete asymptotic

    更新日期:2020-12-23
  • Large‐sample approximations and change testing for high‐dimensional covariance matrices of multivariate linear time series and factor models
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-12-14
    Monika Bours; Ansgar Steland

    Various statistical problems can be formulated in terms of a bilinear form of the covariance matrix. Examples are testing whether coordinates of a high‐dimensional random vector are uncorrelated, constructing confidence intervals for the risk of optimal portfolios or testing for the stability of a covariance matrix, especially for factor models.

    更新日期:2020-12-15
  • Approximate Bayesian inference for a spatial point process model exhibiting regularity and random aggregation
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-12-14
    Ninna Vihrs; Jesper Møller; Alan E. Gelfand

    In this paper, we propose a doubly stochastic spatial point process model with both aggregation and repulsion. This model combines the ideas behind Strauss processes and log Gaussian Cox processes. The likelihood for this model is not expressible in closed form but it is easy to simulate realisations under the model. We therefore explain how to use approximate Bayesian computation (ABC) to carry out

    更新日期:2020-12-15
  • Statistics for Gaussian random fields with unknown location and scale using Lipschitz‐Killing curvatures
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-11-09
    Elena Di Bernardino; Céline Duval

    In the present article we study the average of Lipschitz‐Killing (LK) curvatures of the excursion set of a stationary isotropic Gaussian field X on ℝ 2 . The novelty is that the field can be nonstandard, that is, with unknown mean and variance, which is more realistic from an applied viewpoint. To cope with the unknown location and scale parameters of X, we introduce novel fundamental quantities called

    更新日期:2020-12-15
  • Multivariate conditional transformation models
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-11-09
    Nadja Klein; Torsten Hothorn; Luisa Barbanti; Thomas Kneib

    Regression models describing the joint distribution of multivariate responses conditional on covariate information have become an important aspect of contemporary regression analysis. However, a limitation of such models are the rather simplistic assumptions often made, for example, a constant dependence structure not varying with covariates or the restriction to linear dependence between the responses

    更新日期:2020-12-14
  • Nonparametric extreme conditional expectile estimation
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-11-10
    Stéphane Girard; Gilles Stupfler; Antoine Usseglio‐Carleve

    Expectiles and quantiles can both be defined as the solution of minimization problems. Contrary to quantiles though, expectiles are determined by tail expectations rather than tail probabilities, and define a coherent risk measure. For these two reasons in particular, expectiles have recently started to be considered as serious candidates to become standard tools in actuarial and financial risk management

    更新日期:2020-12-11
  • Feature screening for case‐cohort studies with failure time outcome
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-11-15
    Jing Zhang; Haibo Zhou; Yanyan Liu; Jianwen Cai

    Case‐cohort design has been demonstrated to be an economical and efficient approach in large cohort studies when the measurement of some covariates on all individuals is expensive. Various methods have been proposed for case‐cohort data when the dimension of covariates is smaller than sample size. However, limited work has been done for high‐dimensional case‐cohort data which are frequently collected

    更新日期:2020-12-11
  • Nonparametric volatility change detection
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-11-03
    Maria Mohr; Natalie Neumeyer

    We consider a nonparametric heteroscedastic time series regression model and suggest testing procedures to detect changes in the conditional variance function. The tests are based on a sequential marked empirical process and thus combine classical CUSUM tests from change point analysis with marked empirical process approaches known from goodness‐of‐fit testing. The tests are consistent against general

    更新日期:2020-12-08
  • Maximum pseudo‐likelihood estimation based on estimated residuals in copula semiparametric models
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-11-07
    Marek Omelka; Šárka Hudecová; Natalie Neumeyer

    This paper deals with an estimation of the dependence structure of a multidimensional response variable in the presence of a multivariate covariate. It is assumed that the covariate affects only the marginal distributions through regression models while the dependence structure, which is described by a copula, is unaffected. A parametric estimation of the copula function is considered with focus on

    更新日期:2020-12-07
  • Bias approximations for likelihood‐based estimators
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-11-03
    Ruby Chiu‐Hsing Weng; D. Stephen Coad

    Bias approximation has played an important rôle in statistical inference, and numerous bias calculation techniques have been proposed under different contexts. We provide a unified approach to approximating the bias of the maximum likelihood estimator and the l2 penalized likelihood estimator for both linear and nonlinear models, where the design variables are allowed to be random and the sample size

    更新日期:2020-11-25
  • Issue Information
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-11-17

    No abstract is available for this article.

    更新日期:2020-11-18
  • Local asymptotic properties for Cox‐Ingersoll‐Ross process with discrete observations
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-09-16
    Mohamed Ben Alaya; Ahmed Kebaier; Ngoc Khue Tran

    In this paper, we consider a one‐dimensional Cox‐Ingersoll‐Ross (CIR) process whose drift coefficient depends on unknown parameters. Considering the process discretely observed at high frequency, we prove the local asymptotic normality property in the subcritical case, the local asymptotic quadraticity in the critical case, and the local asymptotic mixed normality property in the supercritical case

    更新日期:2020-11-18
  • A reproducing kernel Hilbert space log‐rank test for the two‐sample problem
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-10-17
    Tamara Fernández; Nicolás Rivera

    Weighted log‐rank tests are arguably the most widely used tests by practitioners for the two‐sample problem in the context of right‐censored data. Many approaches have been considered to make them more robust against a broader family of alternatives, including taking linear combinations, or the maximum among a finite collection of them. In this article, we propose as test statistic the supremum of

    更新日期:2020-11-15
  • Max‐infinitely divisible models and inference for spatial extremes
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-09-03
    Raphaël Huser; Thomas Opitz; Emeric Thibaud

    For many environmental processes, recent studies have shown that the dependence strength is decreasing when quantile levels increase. This implies that the popular max‐stable models are inadequate to capture the rate of joint tail decay, and to estimate joint extremal probabilities beyond observed levels. We here develop a more flexible modeling framework based on the class of max‐infinitely divisible

    更新日期:2020-10-30
  • Parametric versus nonparametric: The fitness coefficient
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-10-05
    Gildas Mazo; François Portier

    Olkin and Spiegelman introduced a semiparametric estimator of the density defined as a mixture between the maximum likelihood estimator and the kernel density estimator. Due to the absence of any leave‐one‐out strategy and the hardness of estimating the Kullback–Leibler loss of kernel density estimate, their approach produces unsatisfactory results. This article investigates an alternative approach

    更新日期:2020-10-30
  • Estimation of change‐point for a class of count time series models
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-08-25
    Yunwei Cui; Rongning Wu; Qi Zheng

    We apply a three‐step sequential procedure to estimate the change‐point of count time series. Under certain regularity conditions, the estimator of change‐point converges in distribution to the location of the maxima of a two‐sided random walk. We derive a closed‐form approximating distribution for the maxima of the two‐sided random walk based on the invariance principle for the strong mixing processes

    更新日期:2020-10-11
  • Functional inference on rotational curves under sample‐specific group actions and identification of human gait
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-08-23
    Fabian J.E. Telschow; Michael R. Pierrynowski; Stephan F. Huckemann

    Inspired by the problem of gait reproducibility (reidentifying individuals across doctor's visits) we develop two‐sample permutation tests under a sample‐specific group action on Lie groups with a bi‐invariant Riemannian metric. These tests rely on consistent estimators and pairwise curve alignment. To this end, we propose Gaussian perturbation models and for the special case of curves on the group

    更新日期:2020-10-11
  • Objective priors in the empirical Bayes framework
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-08-13
    Ilja Klebanov; Alexander Sikorski; Christof Schütte; Susanna Röblitz

    When dealing with Bayesian inference the choice of the prior often remains a debatable question. Empirical Bayes methods offer a data‐driven solution to this problem by estimating the prior itself from an ensemble of data. In the nonparametric case, the maximum likelihood estimate is known to overfit the data, an issue that is commonly tackled by regularization. However, the majority of regularizations

    更新日期:2020-09-29
  • On the assumption of independent right censoring
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-08-19
    Morten Overgaard; Stefan Nygaard Hansen

    Various assumptions on a right‐censoring mechanism to ensure consistency of the Kaplan–Meier and Aalen–Johansen estimators in a competing risks setting are studied. Specifically, eight different assumptions are seen to fall in two categories: a weaker identifiability assumption, which is the weakest possible assumption in a precise sense, and a stronger representativity assumption which ensures the

    更新日期:2020-09-28
  • A new Gini correlation between quantitative and qualitative variables
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-09-03
    Xin Dang; Dao Nguyen; Yixin Chen; Junying Zhang

    We propose a new Gini correlation to measure dependence between a categorical and numerical variables. Analogous to Pearson R2 in ANOVA model, the Gini correlation is interpreted as the ratio of the between‐group variation and the total variation, but it characterizes independence (zero Gini correlation mutually implies independence). Closely related to the distance correlation, the Gini correlation

    更新日期:2020-09-20
  • A goodness‐of‐fit test for the functional linear model with functional response
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-08-16
    Eduardo García‐Portugués; Javier Álvarez‐Liébana; Gonzalo Álvarez‐Pérez; Wenceslao González‐Manteiga

    The functional linear model with functional response (FLMFR) is one of the most fundamental models to assess the relation between two functional random variables. In this article, we propose a novel goodness‐of‐fit test for the FLMFR against a general, unspecified, alternative. The test statistic is formulated in terms of a Cramér–von Mises norm over a doubly projected empirical process which, using

    更新日期:2020-09-14
  • Pseudo likelihood‐based estimation and testing of missingness mechanism function in nonignorable missing data problems
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-09-04
    Xuerong Chen; Guoqing Diao; Jing Qin

    In nonignorable missing response problems, we study a semiparametric model with unspecified missingness mechanism model and a exponential family model for response conditional density. Even though existing methods are available to estimate the parameters in exponential family, estimation or testing of the missingness mechanism model nonparametrically remains to be an open problem. By defining a “synthesis"

    更新日期:2020-09-04
  • Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-09-03
    Matti Vihola; Jouni Helske; Jordan Franks

    We consider importance sampling (IS) type weighted estimators based on Markov chain Monte Carlo (MCMC) targeting an approximate marginal of the target distribution. In the context of Bayesian latent variable models, the MCMC typically operates on the hyperparameters, and the subsequent weighting may be based on IS or sequential Monte Carlo (SMC), but allows for multilevel techniques as well. The IS

    更新日期:2020-09-03
  • Ensemble updating of binary state vectors by maximizing the expected number of unchanged components
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-07-20
    Margrethe Kvale Loe; Håkon Tjelmeland

    The main challenge in ensemble‐based filtering methods is the updating of a prior ensemble to a posterior ensemble. In the ensemble Kalman filter (EnKF), a linear‐Gaussian model is introduced to overcome this issue, and the prior ensemble is updated with a linear shift. In the current article, we consider how the underlying ideas of the EnKF can be applied when the state vector consists of binary variables

    更新日期:2020-09-02
  • On inference for modes under long memory
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-06-23
    Jan Beran; Klaus Telkmann

    We consider inference for local maxima of the marginal density function of strongly dependent linear processes. Weak consistency of the estimated modular set and the number of modes is derived. A uniform reduction principle for kernel density estimators is used to obtain confidence sets for the set of modes. The results can be extended to multivariate modes. Simulations illustrate the asymptotic results

    更新日期:2020-09-01
  • Sufficient dimension reduction based on distance‐weighted discrimination
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-07-23
    Hayley Randall; Andreas Artemiou; Xingye Qiao

    In this paper, we introduce a sufficient dimension reduction (SDR) algorithm based on distance‐weighted discrimination (DWD). Our methods is shown to be robust on the dimension p of the predictors in our problem, and it also utilizes some new computational results in the DWD literature to propose a computationally faster algorithm than previous classification‐based algorithms in the SDR literature

    更新日期:2020-08-28
  • Semiparametric estimation with spatially correlated recurrent events
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-06-28
    Akim Adekpedjou; Sophie Dabo‐Niang

    This article pertains to the analysis of recurrent event data in the presence of spatial correlation. Consider units located at n possibly spatially correlated geographical areas described by their longitude and latitude and monitored for the occurrence of an event that can recur. We propose a new class of semiparametric models for recurrent events that simultaneously account for risk factors and correlation

    更新日期:2020-08-26
  • Ordinal patterns in long‐range dependent time series
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-06-23
    Annika Betken; Jannis Buchsteiner; Herold Dehling; Ines Münker; Alexander Schnurr; Jeannette H.C. Woerner

    We analyze the ordinal structure of long‐range dependent time series. To this end, we use so called ordinal patterns which describe the relative position of consecutive data points. We provide two estimators for the probabilities of ordinal patterns and prove limit theorems in different settings, namely stationarity and (less restrictive) stationary increments. In the second setting, we encounter a

    更新日期:2020-08-10
  • Orientation relationship in finite dimensional space
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-08-10
    Jayant Jha, Atanu Biswas

    In the present article, we discuss the regression of a point on the surface of a unit sphere in d dimensions given a point on the surface of a unit sphere in p dimensions, where p may not be equal to d. Point projection is added to the rotation and linear transformation for regression link function. The identifiability of the model is proved. Then, parameter estimation in this set up is discussed.

    更新日期:2020-08-10
  • Accounting for model uncertainty in multiple imputation under complex sampling
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-06-03
    Gyuhyeong Goh; Jae Kwang Kim

    Multiple imputation provides an effective way to handle missing data. When several possible models are under consideration for the data, multiple imputation is typically performed under a single‐best model selected from the candidate models. This single‐model selection approach ignores the uncertainty associated with the model selection and so leads to underestimation of the variance of multiple imputation

    更新日期:2020-07-28
  • Models and inference for on–off data via clipped Ornstein–Uhlenbeck processes
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-05-31
    Emil Aas Stoltenberg; Nils Lid Hjort

    We introduce a model for recurrent event data subject to left‐, right‐, and intermittent‐censoring. The observations consist of binary sequences (along with covariates) for each individual under study. These sequences are modeled as generated by latent Ornstein–Uhlenbeck processes being above or below certain thresholds. Features of the latent process and the thresholds are taken as functions of covariates

    更新日期:2020-07-24
  • Schwartz‐type model selection for ergodic stochastic differential equation models
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-05-31
    Shoichi Eguchi; Yuma Uehara

    We study theoretical foundation of model comparison for ergodic stochastic differential equation (SDE) models and an extension of the applicable scope of the conventional Bayesian information criterion. Different from previous studies, we suppose that the candidate models are possibly misspecified models, and we consider both Wiener and a pure‐jump Lévy noise‐driven SDE. Based on the asymptotic behavior

    更新日期:2020-07-21
  • Robust estimation for discrete‐time state space models
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-07-17
    William H. Aeberhard; Eva Cantoni; Chris Field; Hans R. Künsch; Joanna Mills Flemming; Ximing Xu

    State space models (SSMs) are now ubiquitous in many fields and increasingly complicated with observed and unobserved variables often interacting in nonlinear fashions. The crucial task of validating model assumptions thus becomes difficult, particularly since some assumptions are formulated about unobserved states and thus cannot be checked with data. Motivated by the complex SSMs used for the assessment

    更新日期:2020-07-17
  • A test for Gaussianity in Hilbert spaces via the empirical characteristic functional
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-05-27
    Norbert Henze; María Dolores Jiménez‐Gamero

    Let X 1,X 2,… be independent and identically distributed random elements taking values in a separable Hilbert space ℍ . With applications for functional data in mind, ℍ may be regarded as a space of square‐integrable functions, defined on a compact interval. We propose and study a novel test of the hypothesis H 0 that X 1 has some unspecified nondegenerate Gaussian distribution. The test statistic

    更新日期:2020-07-06
  • Local quadratic estimation of the curvature in a functional single index model
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-06-28
    Zi Ye; Giles Hooker

    The nonlinear responses of species to environmental variability can play an important role in the maintenance of ecological diversity. Nonetheless, many models use parametric nonlinear terms which pre‐determine the ecological conclusions. Motivated by this concern, we study the estimate of the second derivative (curvature) of the link function in a functional single index model. Since the coefficient

    更新日期:2020-06-28
  • Testing multivariate normality by zeros of the harmonic oscillator in characteristic function spaces
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-06-23
    Philip Dörr; Bruno Ebner; Norbert Henze

    We study a novel class of affine invariant and consistent tests for normality in any dimension in an i.i.d.‐setting. The tests are based on a characterization of the standard d‐variate normal distribution as the unique solution of an initial value problem of a partial differential equation motivated by the harmonic oscillator, which is a special case of a Schrödinger operator. We derive the asymptotic

    更新日期:2020-06-23
  • Linear censored quantile regression: A novel minimum‐distance approach
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-05-31
    Mickaël De Backer; Anouar El Ghouch; Ingrid Van Keilegom

    In this article, we investigate a new procedure for the estimation of a linear quantile regression with possibly right‐censored responses. Contrary to the main literature on the subject, we propose in this context to circumvent the formulation of conditional quantiles through the so‐called “check” loss function that stems from the influential work of Koenker and Bassett (1978). Instead, our suggestion

    更新日期:2020-05-31
  • Failure time studies with intermittent observation and losses to follow‐up
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-05-27
    Richard J. Cook; Jerald F. Lawless

    In health research interest often lies in modeling a failure time process but in many cohort studies failure status is only determined at scheduled assessment times. While the assessment times may be fixed upon study entry, individuals may become lost to follow‐up and miss visits subsequent to the time of loss to follow‐up. We consider a three‐state model to characterize a joint failure and loss to

    更新日期:2020-05-27
  • Multiscale change point detection for dependent data
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-05-14
    Holger Dette; Theresa Eckle; Mathias Vetter

    In this article we study the theoretical properties of the simultaneous multiscale change point estimator (SMUCE) in piecewise‐constant signal models with dependent error processes. Empirical studies suggest that in this case the change point estimate is inconsistent, but it is not known if alternatives suggested in the literature for correlated data are consistent. We propose a modification of SMUCE

    更新日期:2020-05-14
  • Issue Information
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-05-10

    No abstract is available for this article.

    更新日期:2020-05-10
  • Stratified proportional subdistribution hazards model with covariate‐adjusted censoring weight for case‐cohort studies
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-05-07
    Soyoung Kim; Yayun Xu; Mei‐Jie Zhang; Kwang‐Woo Ahn

    The case‐cohort study design is widely used to reduce cost when collecting expensive covariates in large cohort studies with survival or competing risks outcomes. A case‐cohort study dataset consists of two parts: (a) a random sample and (b) all cases or failures from a specific cause of interest. Clinicians often assess covariate effects on competing risks outcomes. The proportional subdistribution

    更新日期:2020-05-07
  • Multivariate extremes over a random number of observations
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-04-29
    Enkelejd Hashorva; Simone A. Padoan; Stefano Rizzelli

    The classical multivariate extreme‐value theory concerns the modeling of extremes in a multivariate random sample, suggesting the use of max‐stable distributions. In this work, the classical theory is extended to the case where aggregated data, such as maxima of a random number of observations, are considered. We derive a limit theorem concerning the attractors for the distributions of the aggregated

    更新日期:2020-04-29
  • Identifiability and estimation of recursive max‐linear models
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-04-28
    Nadine Gissibl; Claudia Klüppelberg; Steffen Lauritzen

    We address the identifiability and estimation of recursive max‐linear structural equation models represented by an edge‐weighted directed acyclic graph (DAG). Such models are generally unidentifiable and we identify the whole class of DAG s and edge weights corresponding to a given observational distribution. For estimation, standard likelihood theory cannot be applied because the corresponding families

    更新日期:2020-04-28
  • Maximum likelihood estimation for totally positive log‐concave densities
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-04-27
    Elina Robeva; Bernd Sturmfels; Ngoc Tran; Caroline Uhler

    We study nonparametric maximum likelihood estimation for two classes of multivariate distributions that imply strong forms of positive dependence; namely log‐supermodular (MTP2) distributions and log‐L♮‐concave (LLC) distributions. In both cases we also assume log‐concavity in order to ensure boundedness of the likelihood function. Given n independent and identically distributed random vectors from

    更新日期:2020-04-27
  • On the identification of individual level pleiotropic, pure direct, and principal stratum direct effects without cross world assumptions
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-04-24
    Jaffer M. Zaidi; Tyler J. VanderWeele

    The analysis of natural direct and principal stratum direct effects has a controversial history in statistics and causal inference as these effects are commonly identified with either untestable cross world independence or graphical assumptions. This article demonstrates that the presence of individual level natural direct and principal stratum direct effects can be identified without cross world independence

    更新日期:2020-04-24
  • Clustering with statistical error control
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-04-17
    Michael Vogt; Matthias Schmid

    This article presents a clustering approach that allows for rigorous statistical error control similar to a statistical test. We develop estimators for both the unknown number of clusters and the clusters themselves. The estimators depend on a tuning parameter α which is similar to the significance level of a statistical hypothesis test. By choosing α, one can control the probability of overestimating

    更新日期:2020-04-23
  • Bayesian variable selection for multioutcome models through shared shrinkage
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-04-16
    Debamita Kundu; Riten Mitra; Jeremy T. Gaskins

    Variable selection over a potentially large set of covariates in a linear model is quite popular. In the Bayesian context, common prior choices can lead to a posterior expectation of the regression coefficients that is a sparse (or nearly sparse) vector with a few nonzero components, those covariates that are most important. This article extends the “global‐local” shrinkage idea to a scenario where

    更新日期:2020-04-23
  • A framework for covariate balance using Bregman distances
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-04-07
    Kevin P. Josey; Elizabeth Juarez‐Colunga; Fan Yang; Debashis Ghosh

    A common goal in observational research is to estimate marginal causal effects in the presence of confounding variables. One solution to this problem is to use the covariate distribution to weight the outcomes such that the data appear randomized. The propensity score is a natural quantity that arises in this setting. Propensity score weights have desirable asymptotic properties, but they often fail

    更新日期:2020-04-23
  • Boosting multiplicative model combination
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-03-31
    Paolo Vidoni

    In this article, we define a new boosting‐type algorithm for multiplicative model combination using as loss function the Hyvärinen scoring rule. In particular, we focus on density estimation problems and the aim is to define a suitable estimator, using a multiplicative combination of elementary density functions, which correspond to simplified or partially specified probability models for the interest

    更新日期:2020-04-23
  • Grenander functionals and Cauchy's formula
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-03-31
    Piet Groeneboom

    Let f ^ n be the nonparametric maximum likelihood estimator of a decreasing density. Grenander characterized this as the left‐continuous slope of the least concave majorant of the empirical distribution function. For a sample from the uniform distribution, the asymptotic distribution of the L2‐distance of the Grenander estimator to the uniform density was derived in an article by Groeneboom and Pyke

    更新日期:2020-04-23
  • Inference for longitudinal data from complex sampling surveys: An approach based on quadratic inference functions
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-02-28
    Laura Dumitrescu; Wei Qian; J. N. K. Rao

    We propose a survey weighted quadratic inference function method for the analysis of data collected from longitudinal surveys, as an alternative to the survey weighted generalized estimating equation method. The procedure yields estimators of model parameters, which are shown to be consistent and have a limiting normal distribution. Furthermore, based on the inference function, a pseudolikelihood ratio

    更新日期:2020-04-23
  • Adaptive estimating function inference for nonstationary determinantal point processes
    Scand. J. Stat. (IF 0.924) Pub Date : 2020-02-20
    Frédéric Lavancier; Arnaud Poinas; Rasmus Waagepetersen

    Estimating function inference is indispensable for many common point process models where the joint intensities are tractable while the likelihood function is not. In this article, we establish asymptotic normality of estimating function estimators in a very general setting of nonstationary point processes. We then adapt this result to the case of nonstationary determinantal point processes, which

    更新日期:2020-04-23
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