Showing papers in "Journal of Nonparametric Statistics in 2019"
TL;DR: Recently, Lad, Sanfilippo, and Agro as mentioned in this paper showed that the measure of entropy has a complementary dual, which is termed extro-expander, and proposed extroexpander as the complementary dual of entropy.
Abstract: Recently, Lad, Sanfilippo, and Agro [(2015), ‘Extropy: Complementary Dual of Entropy’, Statistical Science, 30, 40–58.] showed the measure of entropy has a complementary dual, which is termed extro...
32 citations
TL;DR: In this article, the k-NN estimation of nonparametric regression model for strong mixing functional time series data is investigated and the uniformity of the kNN estimation is established.
Abstract: 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 alm...
19 citations
TL;DR: In this paper, a class of nonparametric Shewhart-type control charts based on a reference sample drawn from the process is presented. The proposed control chart takes advantage of the location of tw...
Abstract: We establish a class of nonparametric Shewhart-type control charts based on a reference sample drawn from the process. The proposed nonparametric control chart takes advantage of the location of tw...
18 citations
TL;DR: In this paper, a new automatic and location-adaptive procedure for estimating regression in a Functional Single-Index Model (FSIM) is developed based on k-Nearest Neighbours (kNN) ideas.
Abstract: 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....
18 citations
TL;DR: In this paper, the authors use the area under an empirical receiver operating characteristic (ROC) curve to test the hypothesis that a predictive index combined with a range of cutoffs performs no better than pure chance in forecasting a binary outcome.
Abstract: We consider using the area under an empirical receiver operating characteristic (ROC) curve to test the hypothesis that a predictive index combined with a range of cutoffs performs no better than pure chance in forecasting a binary outcome. This corresponds to the null hypothesis that the area in question, denoted as AUC, is 1/2. We show that if the predictive index comes from a first stage regression model estimated over the same data set, then testing the null based on standard asymptotic normality results leads to severe size distortion in general settings. We then analytically derive the proper asymptotic null distribution of the empirical AUC in a special case; namely, when the first stage regressors are Bernoulli random variables. This distribution can be utilized to construct a fully in-sample test of H0 : AUC = 1=2 with correct size and more power than out-of-sample tests based on sample splitting, though practical application becomes cumbersome with more than two regressors.
16 citations
TL;DR: In this paper, the authors used Moran's I statistic to evaluate spatial autocorrelation and found that the I statistic has been widely used to evaluate the spatial auto-correlation.
Abstract: Moran's I statistic [Moran, (1950), ‘Notes on Continuous Stochastic Phenomena’, Biometrika, 37, 17–23] has been widely used to evaluate spatial autocorrelation. This paper is concerned with Moran's...
15 citations
TL;DR: In this article, a new estimator of the conditional density and mode when the co-variables are of functional kind is presented, which is a combination of both, the k-Nearest Neighbours procedur...
Abstract: In this paper we present a new estimator of the conditional density and mode when the co-variables are of functional kind. This estimator is a combination of both, the k-Nearest Neighbours procedur...
15 citations
TL;DR: In this paper, the authors estimate the correlation curve under the multiplicative distortion measurement errors setting, where unobservable variables are both distorted in a multiplicative fashion by an observed confounding variable.
Abstract: 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 estimated correlation curve. We conduct Monte Carlo simulation experiments to examine the performance of the proposed estimator. The estimated correlation curve is applied to analyze a real dataset for an illustration.
12 citations
TL;DR: Many distribution-free control charts have been proposed for jointly monitoring location and scale parameters of a continuous distribution when their in-control status is unknown in a controlled environment as mentioned in this paper, where the IC status of the distribution is unknown.
Abstract: 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 a...
11 citations
TL;DR: In this paper, the authors studied the class of bivariate penalised splines that use tensor product splines and a smoothness penalty, and showed that these splines can be used for smoothing smoothness penalties.
Abstract: We study the class of bivariate penalised splines that use tensor product splines and a smoothness penalty. Similar to Claeskens, G., Krivobokova, T., and Opsomer, J.D. [(2009), ‘Asymptotic Propert...
9 citations
TL;DR: A new method for sufficient dimension reduction when both response and predictor are vectors is proposed, using distance covariance, which keeps the model-free advantage, and can fully recover the central subspace even when many predictors are discrete.
Abstract: In this article, we propose a new method for sufficient dimension reduction when both response and predictor are vectors. The new method, using distance covariance, keeps the model-free advantage, ...
TL;DR: A transformation kernel density estimator is developed which is able to handle heavy tailed and bounded data, and is robust to threshold choice, and derive closed form expressions for its asymptotic bias and variance, which demonstrate its good performance in the tail region.
Abstract: It is often critical to accurately model the upper tail behaviour of a random process. Nonparametric density estimation methods are commonly implemented as exploratory data analysis techniques for ...
TL;DR: In this article, the estimation problem of a nonparametric periodic function observed with Levy noises in continuous time was solved using the improved method of the James-Stein improved method.
Abstract: In this paper, we develop the James–Stein improved method for the estimation problem of a nonparametric periodic function observed with Levy noises in continuous time. An adaptive model sel...
TL;DR: The Gini index has been widely used as a measure of income (or wealth) inequality in social sciences as discussed by the authors, and the confidence interval for the difference of two Gini indices from the pair...
Abstract: 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 pair...
TL;DR: In this article, a nonparametric test for testing the independence of time to failure and cause of failure in competing risks set up is presented, and generalised the test to the situation where f...
Abstract: 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 f...
TL;DR: In this paper, the authors designed a test to detect the arrivals of jumps in asset prices contaminated by market microstructure noise, defined by means of the truncated two-scales realised volati...
Abstract: 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 volati...
TL;DR: In this paper, a regression analysis of interval-censored failure time data with noninformative censoring has been proposed, where the mechanism behind the inter-censoring mechanism is investigated.
Abstract: 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 interva...
TL;DR: In this article, a new estimation method was proposed to estimate the nonparametric functions in additive models, where the response is subject to fixed censoring under some regularity conditions.
Abstract: We propose a new estimation method to estimate the nonparametric functions in additive models, where the response is subject to fixed censoring. Under some regularity conditions, we show that the p...
TL;DR: In this paper, a new strategy of estimation for the survival function S, associated to a survival time subject to interval censoring case 2, is proposed, based on a least square estimation.
Abstract: 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 square...
TL;DR: In this paper, two kinds of recursive kernel estimators of f(x) are considered, which is the probability density function of a sequence of ϕ-mixing random variables.
Abstract: 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 suitabl...
TL;DR: A regularisation method and a stepwise procedure for estimating the covariance function and the parametric components efficiently in the varying-coefficient partially linear model that outperforms the existing approaches in simulation studies.
Abstract: 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 ari...
TL;DR: In this paper, the minimax results for the anisotropic two-dimensional functional deconvolution model with the two-parameter fractional Gaussian noise were derived and the lower bounds for the Lp-r...
Abstract: 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-r...
TL;DR: The proposed time-varying coefficient mixed-effects models for continuous multiple time series data and longitudinal data outperform the traditional univariate response models, nonparametric models, and linear mixed effects models in both predicting the response and estimating the coefficient surface based on simulation studies.
Abstract: 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, a...
TL;DR: PID methods are close to standard parametric methods when the errors are iid and normal and for skewed and heavy tailed errors, PID methods are superior to bootstrap and standardParametric methods.
Abstract: For linear regression and related models, a permutation inference distribution (PID) is introduced. Like the confidence distribution in the Bayesian/Fiducial/Frequentist inference framework...
TL;DR: In this paper, an independent and identically distributed (iid) sequence of interest random variables (rv) distributed as T is subject to random censoring by another rv C.
Abstract: Let (Tn)n≥1 be an independent and identically distributed (iid) sequence of interest random variables (rv) distributed as T. In censorship models, T is subject to random censoring by another rv C. ...
TL;DR: In this paper, 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.
Abstract: 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 (n→∞). Finite sample behaviour of the estimates is illustrated by simulations.
TL;DR: A partially time-varying coefficient proportional hazards model is proposed to more flexibly describe covariate effects and has satisfactory asymptotic properties.
Abstract: In survival analysis, we may encounter the following three problems: nonlinear covariate effect, variable selection and measurement error. Existing studies only address one or two of these problems...
TL;DR: Non-ignorable non-response as mentioned in this paper is a common phenomenon in many areas and is a response mechanism that depends on the values of the variable having nonresponse, is the mo...
Abstract: 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 mo...
TL;DR: In this article, a strictly stationary time series is modelled directly, once the variables' realizations fit into a table: no knowledge of a distribution is required other than the prior discretization.
Abstract: A strictly stationary time series is modelled directly, once the variables' realizations fit into a table: no knowledge of a distribution is required other than the prior discretization. A ...
TL;DR: This work proposes two unconventional likelihood-based estimation procedures where the nonignorable missingness mechanism model could be completely bypassed and applies these methods to the children's mental health study.
Abstract: 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...