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Local polynomial regression for pooled response data J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-11-04 Dewei Wang; Xichen Mou; Xiang Li; Xianzheng Huang
We propose local polynomial estimators for the conditional mean of a continuous response when only pooled response data are collected under different pooling designs. Asymptotic properties of these estimators are investigated and compared. Extensive simulation studies are carried out to compare finite sample performance of the proposed estimators under various model settings and pooling strategies
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Score estimation of monotone partially linear index model J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-10-27 Mengshan Xu; Taisuke Otsu
This paper studies semiparametric estimation of a partially linear single index model with a monotone link function. Our estimator is an extension of the score-type estimator developed by Balabdaoui et al. (2019) for the monotone single index model, which profiles out the unknown link function by isotonic regression. An attractive feature of the proposed estimator is that it is free from tuning parameters
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Uniform convergence rate of the kernel regression estimator adaptive to intrinsic dimension in presence of censored data J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-10-17 Salim Bouzebda; Thouria El-hadjali
ABSTRACT The focus of the present paper is on the uniform in bandwidth consistency of kernel-type estimators of the regression function E ( Ψ ( Y ) ∣ X = x ) derived by modern empirical process theory, under weaker conditions on the kernel than previously used in the literature. Our theorems allow data-driven local bandwidths for these statistics. We extend existing uniform bounds on kernel regression
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Robust location estimators in regression models with covariates and responses missing at random J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-11-04 Ana M. Bianco; Graciela Boente; Wenceslao González-Manteiga; Ana Pérez-González
This paper deals with robust marginal estimation under a general regression model when missing data occur in the response and also in some covariates. The target is a marginal location parameter given through an M-functional. To obtain robust Fisher-consistent estimators, properly defined marginal distribution function estimators are considered. These estimators avoid the bias due to missing values
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Statistical estimation for heteroscedastic semiparametric regression model with random errors J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-11-06 Liwang Ding; Ping Chen
This paper is concerned with the estimating problem of heteroscedastic semiparametric regression model. We investigate the asymptotic normality for wavelet estimators of the slope parameter and the nonparametric component in the case of known error variance with ϕ-mixing random errors. Also, when the error variance is unknown, the asymptotic normality for the estimators of the slope parameter and the
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A model-free conditional screening approach via sufficient dimension reduction J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-11-03 Lei Huo; Xuerong Meggie Wen; Zhou Yu
ABSTRACT Conditional variable screening arises when researchers have prior information regarding the importance of certain predictors. It is natural to consider feature screening methods conditioning on these known important predictors. Barut, E., Fan, J., and Verhasselt, A. [(2016), ‘Conditional Sure Independence Screening’, Journal of the American Statistical Association, 111, 1266–1277] proposed
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Flexible weighted dirichlet process mixture modelling and evaluation to address the problem of forecasting return distribution J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-10-23 Peng Sun; Inyoung Kim; Kiahm Lee
ABSTRACT Forecasting volatility has been widely addressed in the fields of finance, environmetrics, and other areas involving massive time series. The important part of addressing this problem is how to specify the error term's distribution. With a weaker distribution assumption, we achieve greater model flexibility. In this paper, we present a flexible semiparametric Bayesian framework to address
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Local asymptotic inference for nonparametric regression with censored survival data J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-10-30 Yanyan Liu; Guangcai Mao; Xingqiu Zhao
We consider a penalised nonparametric estimation of the relative risk function in the Cox proportional hazards model for survival data with right censoring. We derive the convergence rate, functional Bahadur representation (FBR) and local asymptotic normality of the nonparametric estimator by using reproducing kernel Hilbert space, counting process and empirical process theory. The new theoretical
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New empirical likelihood inference for the mean residual life with length-biased and right-censored data J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-11-04 Kangni Alemdjrodo; Yichuan Zhao
ABSTRACT The mean residual life (MRL) function for a given random variable T is the expected remaining lifetime of T after a fixed time point t. It is of great interest in survival analysis, reliability, actuarial applications, duration modelling, etc. Liang, Shen, and He [‘Likelihood Ratio Inference for Mean Residual Life of Length-biased Random Variable’, Acta Mathematicae Applicatae Sinica, English
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Local bandwidth selection for kernel density estimation in a bifurcating Markov chain model J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-07-15 S. Valère Bitseki Penda; Angelina Roche
We propose an adaptive estimator for the stationary distribution of a bifurcating Markov Chain on R d . Bifurcating Markov chains (BMC for short) are a class of stochastic processes indexed by regular binary trees. A kernel estimator is proposed whose bandwidths are selected by a method inspired by the works of Goldenshluger and Lepski [(2011), ‘Bandwidth Selection in Kernel Density Estimation: Oracle
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Intermediate efficiency of some weighted goodness-of-fit statistics J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-07-12 Bogdan Ćmiel; Tadeusz Inglot; Teresa Ledwina
This paper introduces and investigates powerful omnibus test for uniformity and compares it with some classical and recent solutions. All statistics under consideration are weighted functionals of the classical empirical process. The goal is to provide a quantitative comparison of tests under consideration and to study real possibilities of using them to detect departures from the hypothesised distribution
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Semiparametric efficient inferences for generalised partially linear models J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-07-14 Jafer Rahman; Shihua Luo; Yawen Fan; Xiaohui Liu
In this paper, we consider semiparametric efficient inferences in the generalised partially linear models. A novel bias-corrected estimating procedure and a bias-corrected empirical log-likelihood ratio are developed, respectively, for point estimation and confidence regions for parameters of interest. Under mild conditions, the resulting likelihood ratio is shown to be standard chi-squared distributed
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Single functional index quantile regression under general dependence structure J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-07-25 Mohamed Chaouch; Amina Angelika Bouchentouf; Aboubacar Traore; Abbes Rabhi
The main purpose of this paper is to estimate, semi-parametrically, the quantiles of a conditional distribution when the response is a real-valued random variable subject to a right-censorship phenomenon and the predictor takes values in an infinite dimensional space. We assume that the explanatory and the response variables are linked through a single-index structure. First, we introduce a kernel-type
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Asymptotic tail approximations for some nonparametric test statistics J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-07-25 Cyrille Joutard
We give some asymptotic approximations for the upper tail probabilities of two nonparametric test statistics, namely the Wilcoxon signed-rank statistic and Kendall's tau coefficient. Then, we provide numerical comparisons, by comparing the exact probabilities with a normal approximation, a classical saddlepoint approximation, a Lugannani and Rice saddlepoint approximation and an Edgeworth expansion
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Two-step combined nonparametric likelihood estimation of misspecified semiparametric models J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-07-28 Francesco Bravo
This paper proposes to estimate possibly misspecified semiparametric estimating equations models using a two-step combined nonparametric likelihood method. The method uses in the first step the plug in principle and replaces the infinite dimensional parameter with a consistent estimator. In the second step an estimator for the finite dimensional parameter is obtained by combining exponential tilting
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Testing for additivity in nonparametric heteroscedastic regression models J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-07-28 Adriano Zanin Zambom; Jongwook Kim
This paper introduces a novel hypothesis test for additivity in nonparametric regression models. Inspired by recent advances in the asymptotic theory of analysis of variance when the number of factor levels is large, we develop a test statistic that checks for possible nonlinear relations between the available predictors and the residuals from fitting the additive model. The asymptotic distribution
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Bootstrapping covariance operators of functional time series J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-06-01 Olimjon Sh. Sharipov; Martin Wendler
For testing hypothesis on the covariance operator of functional time series, we suggest to use the full functional information and to avoid dimension reduction techniques. The limit distribution follows from the central limit theorem of the weak convergence of the partial sum process in general Hilbert space applied to the product space. In order to obtain critical values for tests, we generalise bootstrap
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Higher-order bias corrections for kernel type density estimators on the unit or semi-infinite interval J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-05-27 Gaku Igarashi; Yoshihide Kakizawa
For the data of size n from the unit or semi-infinite interval, several asymmetric kernel density estimators, having the mean integrated squared errors of order O ( n − 4 / 5 ) or O ( n − 8 / 9 ) , are available in the literature. We develop more higher-order bias-corrected estimators, achieving the order O ( n − 4 p / ( 4 p + 1 ) ) , where p ≥ 2 is a given integer. We illustrate the finite sample
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Kernel smoothed probability mass functions for ordered datatypes J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-05-12 Jeffrey S. Racine; Qi Li; Karen X. Yan
We propose a kernel function for ordered categorical data that overcomes limitations present in ordered kernel functions appearing in the literature on the estimation of probability mass functions for multinomial ordered data. Some limitations arise from assumptions made about the support of the underlying random variable. Furthermore, many existing ordered kernel functions lack a particularly appealing
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Uniform consistency and uniform in bandwidth consistency for nonparametric regression estimates and conditional U-statistics involving functional data J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-05-06 Salim Bouzebda; Boutheina Nemouchi
W. Stute [(1991), Annals of Probability, 19, 812–825] introduced a class of so-called conditional U-statistics, which may be viewed as a generalisation of the Nadaraya–Watson estimates of a regression function. Stute proved their strong pointwise consistency to m(t):=E[ϕ(Y1,…,Ym)|(X1,…,Xm)=t],for t∈Rdm. We apply the methods developed in Dony and Mason [(2008), Bernoulli, 14(4), 1108–1133] to establish
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Data-driven local polynomial for the trend and its derivatives in economic time series J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-05-06 Yuanhua Feng; Thomas Gries; Marlon Fritz
The main purpose of this paper is the development of data-driven iterative plug-in algorithms for local polynomial estimation of the trend and its derivatives under dependent errors. Furthermore, a data-driven lag-window estimator for the variance factor in the bandwidth is proposed so that the nonparametric stage is carried out without any parametric assumption on the stationary errors. Analysis of
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Goodness-of-fit test for hazard rate J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-05-06 Ralph-Antoine Vital; Prakash Patil
In Pharmacokinetic (PK) and Pharmacodynamic (PD), the hazard rate functions play a central role in modelling time-to-event data. In the context of assessing the appropriateness of a given parametric hazard model, Huh, Y., and Hutmacher, M. [(2016), ‘Application of a Hazard-based Visual Predictive Check to Evaluate Parametric Hazard Models’, Journal of Pharmacokinetics and Pharmacodynamics, 43, 57–71]
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Nonparametric estimation of volatility function in the jump-diffusion model with noisy data J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-04-28 Xu-Guo Ye; Yan-Yong Zhao; Kong-Sheng Zhang
In this article, we propose a two-step approach to estimate the volatility function of a jump-diffusion model in noisy data setting. The preaveraging method and threshold technique is used to remove microstructure noise and jumps, respectively. The newly proposed estimator is shown to be consistent and asymptotically normal. A simulation study and a real data application are undertaken to assess the
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Spatial autoregressive partially linear varying coefficient models J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-04-28 Jingru Mu; Guannan Wang; Li Wang
In this article, we consider a class of partially linear spatially varying coefficient autoregressive models for data distributed over complex domains. We propose approximating the varying coefficient functions via bivariate splines over triangulation to deal with the complex boundary of the spatial domain. Under some regularity conditions, the estimated constant coefficients are asymptotically normally
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Efficient pseudo-Gaussian and rank-based detection of random regression coefficients J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-04-08 Mohamed Fihri; Abdelhadi Akharif; Amal Mellouk; Marc Hallin
Random coefficient regression models are the regression counterparts of the classical random effects models in Analysis of Variance and panel data analysis. While several heuristic methods have been proposed for the detection of such random regression coefficients, little is known on their optimality properties. Based on a nonstandard ULAN property, we are proposing locally asymptotically optimal (in
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Stationarity test based on density approach J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-04-08 Ji Eun Moon; Cheolyong Park; Jeongcheol Ha; Sun Young Hwang; Tae Yoon Kim
It is well known that a neighbourhood problem exists between stationarity and random walk with correlated error for any finite sample size n. That is, any stationary process is approximated by random walk with correlated error for any finite n. Hence, one cannot distinguish between them easily. In this article, we propose a stationarity test based on nonparametric density that resolves the neighbourhood
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A multiply robust Mann-Whitney test for non-randomised pretest-posttest studies with missing data J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-03-02 Shixiao Zhang; Peisong Han; Changbao Wu
Pretest-posttest studies are a commonly used design by social scientists, medical and health researchers to examine the effect of a treatment or an intervention. We propose an empirical likelihood based Mann-Whitney test on the equality of the response distribution functions of the treatment and control arms for non-randomised pretest-posttest studies with missing responses. The proposed test is multiply
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High-dimensional rank-based inference J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-02-11 Xiaoli Kong; Solomon W. Harrar
Existing high-dimensional inferential methods for comparing multiple groups test hypotheses are formulated in terms of mean vectors or location parameters. These methods are applicable mainly for metric data. Furthermore, the mean-based methods assume that moments exist and the nonparametric (location-based) ones assume elliptical-contoured distributions for the populations. In this paper, a fully
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Estimation of additive frontier functions with shape constraints J. Nonparametr. Stat. (IF 0.607) Pub Date : 2020-02-11 Lu Wang; Lan Xue; Lijian Yang
Production frontier is an important concept in modern economics and has been widely used to measure production efficiency. Existing nonparametric frontier models often only allow one or low-dimensional input variables due to ‘curse-of-dimensionality’. In this paper we propose a flexible additive frontier model which quantifies the effects of multiple input variables on the maximum output. In addition
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Nonparametric tests for transition probabilities in nonhomogeneous Markov processes. J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-12-19 Giorgos Bakoyannis
This paper proposes nonparametric two-sample tests for the direct comparison of the probabilities of a particular transition between states of a continuous time non-homogeneous Markov process with a finite state space. The proposed tests are a linear nonparametric test, an L 2-norm-based test and a Kolmogorov-Smirnov-type test. Significance level assessment is based on rigorous procedures, which are
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Nonparametric survival function estimation for data subject to interval censoring case 2 J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-09-25 Olivier Bouaziz, Elodie Brunel, Fabienne Comte
In this paper, we propose a new strategy of estimation for the survival function S, associated to a survival time subject to interval censoring case 2. Our method is based on a least squares contrast of regression type with parameters corresponding to the coefficients of the development of S on an orthonormal basis. We obtain a collection of projection estimators where the dimension of the projection
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Estimators based on unconventional likelihoods with nonignorable missing data and its application to a children's mental health study J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-09-18 Jiwei Zhao, Chi Chen
Nonignorable missing data is common in studies where the outcome is relevant to the subject's behaviour. Ibrahim, Lipsitz, and Horton [(2001), ‘Using Auxiliary Data for Parameter Estimation with Non-ignorably Missing Outcomes’, Journal of the Royal Statistical Society: Series C (Applied Statistics), 50, 361–373] fitted a logistic regression for a binary outcome subject to nonignorable missing data
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Quantile estimation of partially varying coefficient model for panel count data with informative observation times J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-09-17 Weiwei Wang, Xianyi Wu, Xiaobing Zhao, Xian Zhou
Panel count data frequently arise in various applications such as medical research, social sciences and so on. In this paper, a partially varying coefficient model of the panel count data with informative observation times is developed to accommodate the nonlinear interact effects between covariates. For statistical inference of the unknown parameters, quantile regression approaches are proposed, in
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Spatially adaptive binary classifier using B-splines and total variation penalty J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-09-10 Kwan-Young Bak, Jae-Hwan Jhong, Ja-Yong Koo
This paper reports on our study of a binary classifier based on B-splines and the total variation penalty. The decision boundary of the proposed classifier is obtained using a variant of the hinge loss function. We restrict our focus to a two-dimensional predictor space to analyse the theoretical behaviour of the spline decision curve estimator. Theoretical investigation shows that the proposed estimator
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Efficient semiparametric regression for longitudinal data with regularised estimation of error covariance function J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-08-08 Shengji Jia, Chunming Zhang, Hulin Wu
Improving estimation efficiency for regression coefficients is an important issue in the analysis of longitudinal data, which involves estimating the covariance matrix of errors. But challenges arise in estimating the covariance matrix of longitudinal data collected at irregular or unbalanced time points. In this paper, we develop a regularisation method for estimating the covariance function and a
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Reduce the computation in jackknife empirical likelihood for comparing two correlated Gini indices J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-08-06 Kangni Alemdjrodo, Yichuan Zhao
The Gini index has been widely used as a measure of income (or wealth) inequality in social sciences. To construct a confidence interval for the difference of two Gini indices from the paired samples, Wang and Zhao [‘Jackknife Empirical Likelihood for Comparing Two Gini Indices’, The Canadian Journal of Statistics, 44(1), 102–119] used a profile jackknife empirical likelihood. However, the computing
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A test of exponentiality against ℳ alternatives J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-07-22 Priyanka Majumder, Murari Mitra
This paper introduces a test for detecting moment generating function order dominance. We develop a family of scale-invariant test statistics based on the weighted integrals of the difference between the empirical moment generating function and the mgf of an appropriate exponential distribution. The asymptotic distributions of our test statistics are derived and consistency of the test is established
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Construction of credible intervals for nonlinear regression models with unknown error distributions J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-07-22 Ji-Yeon Yang, Jungmo Yoon
There has been continuing interest in Bayesian regressions in which no parametric assumptions are imposed on the error distribution. In this study, we consider semiparametric Bayesian nonlinear regression models. We do not impose a parametric form for the likelihood function. Instead, we treat the true density function of error terms as an infinite-dimensional nuisance parameter and estimate it nonparametrically
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Detecting price jumps in the presence of market microstructure noise J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-07-17 Yucheng Sun
In this paper we design a test to detect the arrivals of jumps in asset prices contaminated by market microstructure noise. This test is defined by means of the truncated two-scales realised volatility estimator, recently introduced in Brownlees, Nualart, and Sun [2019, ‘On the Estimation of Integrated Volatility in the Presence of Jumps and Microstructure Noise’, https://papers.ssrn.com/sol3/papers
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Permutation inference distribution for linear regression and related models J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-06-20 Qiang Wu, Paul Vos
For linear regression and related models, a permutation inference distribution (PID) is introduced. Like the confidence distribution in the Bayesian/Fiducial/Frequentist inference framework, the PID allows the construction of both confidence intervals and p-values. For two-sample problems and pairwise comparisons in ANOVA models, a fast Fourier transformation method can be used to find the exact PID
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On double-index dimension reduction for partially functional data J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-06-20 Guangren Yang, Hongmei Lin, Heng Lian
In this note, we consider the situation where we have a functional predictor as well as some more traditional scalar predictors, which we call the partially functional problem. We propose a semiparametric model based on sufficient dimension reduction, and thus our main interest is in dimension reduction although prediction can be carried out at a second stage. We establish root-n consistency of the
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Hierarchical time-varying mixed-effects models in high-dimensional time series and longitudinal data studies J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-06-19 Jinglan Li, Zhengjun Zhang
We propose time-varying coefficient mixed-effects models for continuous multiple time series data and longitudinal data. The challenge is how to simultaneously display serial, clustering, and multivariate attributes of the data set, to which the routinely assumed two-level hierarchical model and univariate response models are not able to apply. Asymptotic properties of the proposed methods are established
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A new nonparametric monitoring of data streams for changes in location and scale via Cucconi statistic J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-06-19 Dongdong Xiang, Shulin Gao, Wendong Li, Xiaolong Pu, Wen Dou
Many distribution-free control charts have been proposed for jointly monitoring location and scale parameters of a continuous distribution when their in-control (IC) status are unknown in advance. Unfortunately, most existing methods require relatively large amount of historical observations to estimate the IC parameters or to activate the control chart, and batch observations to construct the charting
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Regression analysis of informatively interval-censored failure time data with semiparametric linear transformation model J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-06-08 Da Xu, Shishun Zhao, Tao Hu, Jianguo Sun
Regression analysis of interval-censored failure time data with noninformative censoring has been widely investigated and many methods have been proposed. Sometimes the mechanism behind the interval censoring may be informative and several approaches have also been developed for this latter situation. However, all of these existing methods are for single models and it is well known that in many situations
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Adjusting for baseline information in comparing the efficacy of treatments using bivariate varying-coefficient models J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-06-08 Xiaomeng Niu, Hyunkeun Ryan Cho
In biomedical studies, patients' reaction to the treatment can be different depending on their health condition at baseline. In this paper, we develop a bivariate varying-coefficient regression model for longitudinal data with the baseline outcome. The proposed model enables the exploration of the dynamic trend of response variables over time and to provide an effective treatment based on an individual's
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Integral generators of Archimedean n-copulas J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-06-06 Włodzimierz Wysocki
We introduce two algebraic structures induced by an Archimedean n-copula: a one-parameter group of transformations and a one-parameter local group of transformations. We then study the natural functional characteristics (integral generators) of these structures. We discuss differential equations joining the integral generators and the additive generators of copulas. We show that the algebraic structures
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Improved robust model selection methods for a Lévy nonparametric regression in continuous time J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-04-26 E. A. Pchelintsev, V. A. Pchelintsev, S. M. Pergamenshchikov
In this paper, we develop the James–Stein improved method for the estimation problem of a nonparametric periodic function observed with Lévy noises in continuous time. An adaptive model selection procedure based on the weighted improved least squares estimates is constructed. The improvement effect for nonparametric models is studied. It turns out that in non-asymptotic setting the accuracy improvement
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Anisotropic functional deconvolution with long-memory noise: the case of a multi-parameter fractional Wiener sheet J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-04-16 Rida Benhaddou, Qing Liu
We look into the minimax results for the anisotropic two-dimensional functional deconvolution model with the two-parameter fractional Gaussian noise. We derive the lower bounds for the Lp-risk, 1≤p<∞, and taking advantage of the Riesz poly-potential, we apply a wavelet-vaguelette expansion to de-correlate the anisotropic fractional Gaussian noise. We construct an adaptive wavelet hard-thresholding
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Spline density estimation and inference with model-based penalties J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-04-15 Jian Shi, Anna Liu, Yuedong Wang
In this paper we propose model-based penalties for smoothing spline density estimation and inference. These model-based penalties incorporate indefinite prior knowledge that the density is close to, but not necessarily in a family of distributions. We will use the Pearson and generalisation of the generalised inverse Gaussian families to illustrate the derivation of penalties and reproducing kernels
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Improved statistical inference on semiparametric varying-coefficient partially linear measurement error model J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-04-11 Zhihua Sun, Yifan Jiang, Xue Ye
In this paper, we consider the estimation and goodness-of-fit test of a semiparametric varying-coefficient partially linear (SVCPL) model when both responses and part of covariates are measured with error. It is assumed that the true variables are measurable functions of some auxiliary variables. The often-used assumptions on the measurement error, such as a known error variance, a known distribution
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Cytometry inference through adaptive atomic deconvolution J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-04-02 Manon Costa, Sébastien Gadat, Pauline Gonnord, Laurent Risser
In this paper, we consider a statistical estimation problem known as atomic deconvolution. Introduced in reliability, this model has a direct application when considering biological data produced by flow cytometers. From a statistical point of view, we aim at inferring the percentage of cells expressing the selected molecule and the probability distribution function associated with its fluorescence
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A distribution-free approach for selecting better treatment through an ethical allocation J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-03-28 Radhakanta Das
The present article provides a statistical inference on comparative performances of two treatments in a clinical trial under a two-stage adaptive allocation design. Suppose a fixed number (2m+n, say) of subjects are available for treatment by any of the two competing treatments, say, A and B for a particular ailment. As per the proposed allocation design, 2m incoming subjects are randomised equally
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Uniform consistency rate of kNN regression estimation for functional time series data J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-03-01 Nengxiang Ling, Shuyu Meng, Philippe Vieu
In this paper, we investigate the k-nearest neighbours (kNN) estimation of nonparametric regression model for strong mixing functional time series data. More precisely, we establish the uniform almost complete convergence rate of the kNN estimator under some mild conditions. Furthermore, a simulation study and an empirical application to the real data analysis of sea surface temperature (SST) are carried
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Orthogonal series density estimation for complex surveys J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-03-01 Shangyuan Ye, Ye Liang, Ibrahim A. Ahmad
We propose an orthogonal series density estimator for complex surveys, where samples are neither independent nor identically distributed. The proposed estimator is proved to be design-unbiased and asymptotically design-consistent. The asymptotic normality is proved under both design and combined spaces. Two data driven estimators are proposed based on the proposed oracle estimator. We show the efficiency
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Correlation curve estimation for multiplicative distortion measurement errors data J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-02-18 Zhenghui Feng, Yujie Gai, Jun Zhang
A correlation curve measures the strength of the association between two variables locally at different values of covariate. This paper studies how to estimate the correlation curve under the multiplicative distortion measurement errors setting. The unobservable variables are both distorted in a multiplicative fashion by an observed confounding variable. We obtain asymptotic normality results for the
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Estimation of extreme quantiles in a simulation model J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-01-22 Michael Kohler, Adam Krzyżak
A simulation model with an outcome Y=m(X) is considered, where X is an Rd-valued random variable and m:Rd→R is a smooth function. Estimates of the αn-quantile qm(X),αn of m(X) based on surrogate model of m and on importance sampling are constructed which use at most n evaluations of the function m. Results concerning the rate of convergence of the estimates are derived in case that αn→1 (n→∞) and n⋅(1−αn)→0
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Semiparametric likelihood for estimating equations with non-ignorable non-response by non-response instrument J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-01-22 Ji Chen, Fang Fang
Non-response or missing data is a common phenomenon in many areas. Non-ignorable non-response, a response mechanism that depends on the values of the variable having non-response, is the most difficult type of non-response to handle. This paper considers statistical inference of unknown parameters in estimating equations (EEs) when the variable of interest has non-ignorable non-response. By utilising
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Automatic and location-adaptive estimation in functional single-index regression J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-01-18 Silvia Novo, Germán Aneiros, Philippe Vieu
This paper develops a new automatic and location-adaptive procedure for estimating regression in a Functional Single-Index Model (FSIM). This procedure is based on k-Nearest Neighbours (kNN) ideas. The asymptotic study includes results for automatically data-driven selected number of neighbours, making the procedure directly usable in practice. The local feature of the kNN approach insures higher predictive
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On the rates of asymptotic normality for recursive kernel density estimators under ϕ-mixing assumptions J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-01-16 Mengmei Xi, Xuejun Wang
In this paper, we mainly consider two kinds of recursive kernel estimators of f(x), which is the probability density function of a sequence of ϕ-mixing random variables {Xi,i≥1}. Under some suitable conditions, we establish the convergence rates of asymptotic normality for the two recursive kernel estimators fˆn(x)=(1/nbn)∑j=1nbj−1/2K((x−Xj)/bj) and f~n(x)=(1/n)∑j=1n(1/bj)K((x−Xj)/bj). In particular
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Test for independence between time to failure and cause of failure in competing risks with k causes J. Nonparametr. Stat. (IF 0.607) Pub Date : 2019-01-03 S. Anjana, Isha Dewan, K. K. Sudheesh
In this paper, we develop a simple nonparametric test for testing the independence of time to failure and cause of failure in competing risks set up. We generalise the test to the situation where failure data is right censored. We obtain the asymptotic distribution of the test statistics for complete and censored data. The efficiency loss due to censoring is studied using Pitman efficiency. The performance
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