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Regression estimation for continuous-time functional data processes with missing at random response J. Nonparametr. Stat. (IF 1.2) Pub Date : 2024-03-22 Mohamed Chaouch, Naâmane Laïb
In this paper, we are interested in nonparametric kernel estimation of a generalised regression function based on an incomplete sample (Xt,Yt,ζt)t∈[0,T] copies of a continuous-time stationary and e...
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Enhanced doubly robust estimation with concave link functions for estimands in clinical trials J. Nonparametr. Stat. (IF 1.2) Pub Date : 2024-03-12 Junyi Zhang, Ao Yuan, Ming T. Tan
For observational studies or clinical trials not fully randomised, the baseline covariates are often not balanced between the treatment and control groups. In this case, the traditional estimates o...
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Bayesian semi-parametric estimation of compound inhomogeneous Poisson processes for ultra-high frequency financial transaction data J. Nonparametr. Stat. (IF 1.2) Pub Date : 2024-03-12 Masaru Hashimoto, Peter J. Lenk
Marked point processes provide a flexible framework for studying ultra-high frequency financial data that records the time and price for each transaction. This paper estimates compound, inhomogeneo...
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A robust estimation based on penalised regularisation for the varying-coefficient additive model J. Nonparametr. Stat. (IF 1.2) Pub Date : 2024-03-07 Yaxuan Zhao, Yuehan Yang
To effectively handle functional data and longitudinal data, we propose a robust estimation approach based on penalised regularisation with the framework of the varying-coefficient additive model. ...
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Checking normality of model errors under additive distortion measurement errors J. Nonparametr. Stat. (IF 1.2) Pub Date : 2024-03-05 Mengyao Li, Jiangshe Zhang, Jun Zhang, Yan Zhou
We study the goodness-of-fit tests for checking the normality of the model errors under the additive distortion measurement error settings. Neither the response variable nor the covariates can be d...
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A powerful nonparametric test of the effect of dementia duration on mortality J. Nonparametr. Stat. (IF 1.2) Pub Date : 2024-03-05 Rafael Weißbach, Lucas Radloff, Constantin Reinke, G. Doblhammer
A continuous-time multi-state history is semi-Markovian, if an intensity to migrate from one state into a second, depends on the duration in the first state. Such duration can be formalised as mark...
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Robust sufficient dimension reduction via α-distance covariance J. Nonparametr. Stat. (IF 1.2) Pub Date : 2024-02-19 Hsin-Hsiung Huang, Feng Yu, Teng Zhang
We introduce a novel sufficient dimension-reduction (SDR) method which is robust against outliers using α-distance covariance (dCov) in dimension-reduction problems. Under very mild conditions on t...
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Editorial J. Nonparametr. Stat. (IF 1.2) Pub Date : 2024-02-16 Stathis Paparoditis, Ingrid Van Keilegom
Published in Journal of Nonparametric Statistics (Vol. 36, No. 1, 2024)
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Good-bootstrap: simultaneous confidence intervals for large alphabet distributions J. Nonparametr. Stat. (IF 1.2) Pub Date : 2024-02-12 Daniel Marton, Amichai Painsky
Simultaneous confidence intervals (SCI) for multinomial proportions are a corner stone in count data analysis and a key component in many applications. A variety of schemes were introduced over the...
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Empirical likelihood based confidence regions for functional of copulas J. Nonparametr. Stat. (IF 1.2) Pub Date : 2024-02-05 Salim Bouzebda, Amor Keziou
In the present paper, we are mainly concerned with the statistical inference for the functional of nonparametric copula models satisfying linear constraints. The asymptotic properties of the obtain...
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Nonparametric Bayes multiresolution testing for high-dimensional rare events J. Nonparametr. Stat. (IF 1.2) Pub Date : 2024-02-01 Jyotishka Datta, Sayantan Banerjee, David B. Dunson
In a variety of application areas, there is interest in assessing evidence of differences in the intensity of event realizations between groups. For example, in cancer genomic studies collecting da...
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A double exponential gamma-frailty model for clustered survival data J. Nonparametr. Stat. (IF 1.2) Pub Date : 2024-01-25 Mengqi Xie, Jie Zhou, Lei Liu
We propose a double exponential gamma-frailty model for clustered survival data. This model addresses the limitation of shared gamma-frailty models, where the marginal effects of covariates diminis...
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Asymptotic normality of kernel density estimation for mixing high-frequency data J. Nonparametr. Stat. (IF 1.2) Pub Date : 2024-01-25 Shanchao Yang, Lanjiao Qin, Y. Wang, X. Yang
High-frequency data is widely used and studied in many fields. In this paper, the asymptotic normality of kernel density estimator under ρ-mixing high-frequency data is studied. We first derive som...
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A process of dependent quantile pyramids J. Nonparametr. Stat. (IF 1.2) Pub Date : 2024-01-22 Hyoin An, Steven N. MacEachern
Despite the practicality of quantile regression (QR), simultaneous estimation of multiple QR curves continues to be challenging. We address this problem by proposing a Bayesian nonparametric framew...
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Generalised local polynomial estimators of smooth functionals of a distribution function with nonnegative support J. Nonparametr. Stat. (IF 1.2) Pub Date : 2024-01-17 Y. P. Chaubey, K. Ghoudi, N. Laïb
This paper introduces generalised smooth asymmetric kernel estimators for smooth functionals with non-negative support. More precisely, for x∈[0,∞), and for a functional, Φ(x,F) of the distribution...
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Subgroup detection in the heterogeneous partially linear additive Cox model J. Nonparametr. Stat. (IF 1.2) Pub Date : 2024-01-12 Tingting Cai, Tao Hu
In the analysis of survival data, it is crucial to consider individual heterogeneities related to therapy, gender, and genetics as they can impact the validity of conclusions. The heterogeneous par...
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PROFIT: projection-based test in longitudinal functional data J. Nonparametr. Stat. (IF 1.2) Pub Date : 2024-01-03 Salil Koner, So Young Park, Ana-Maria Staicu
In many modern applications, a dependent functional response is observed for each subject over repeated time, leading to longitudinal functional data. In this paper, we propose a novel statistical ...
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An adaptive test based on Kendall's tau for independence in high dimensions J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-12-28 Xiangyu Shi, Yuanyuan Jiang, Jiang Du, Zhuqing Miao
We consider testing the mutual independency for high-dimensional data. It is known that L2-type statistics have lower power under sparse alternatives and L∞-type statistics have lower power under d...
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Approximate Bayesian computation with semiparametric density ratio model J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-12-14 Weixuan Zhu, Tiantian Zuo, Chunlin Wang
Approximate Bayesian computation (ABC) is a likelihood-free inference method commonly employed for statistical inference in models with unknown or complex likelihood functions. ABC estimates the po...
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Oracle-efficient estimation for the mean function of missing covariate data based on noparametrically estimated selection probabilities J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-12-11 Li Cai, Yao Yao, Suojin Wang
A weighted local linear estimator for the mean function is constructed based on nonparametrically estimated selection probabilities when covariates are missing at random. The weighted estimator is ...
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Density derivative estimation using asymmetric kernels J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-12-11 Benedikt Funke, Masayuki Hirukawa
This paper studies the problem of estimating the first-order derivative of an unknown density with support on R+ or [0,1]. Nonparametric density derivative estimators smoothed by the asymmetric, ga...
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Nonparametric estimation of linear multiplier in SDEs driven by general Gaussian processes J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-12-05 B. L. S. Prakasa Rao
We investigate the asymptotic properties of a kernel-type nonparametric estimator of the linear multiplier in models governed by a stochastic differential equation driven by a general Gaussian process.
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Integrated log-rank test J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-11-27 John O'Quigley
The log-rank test can be viewed as nonparametric from the standpoint of a series of 2×2 tables, as semi-parametric from the standpoint of the proportional hazards model and as parametric from the v...
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A bootstrap functional central limit theorem for time-varying linear processes J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-11-27 Carina Beering, Anne Leucht
We provide a functional central limit theorem for a broad class of smooth functions for possibly non-causal multivariate linear processes with time-varying coefficients. Since the limiting processe...
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Home-range estimation under a restricted sample scheme J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-11-10 Alejandro Cholaquidis, Ricardo Fraiman, Manuel Hernández-Banadik
New continuous-time models and statistical methods have been developed to estimate some sets related to animal movement, such as the home-range and the core-area among others, when the information ...
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Regression analysis of multivariate interval-censored failure time data with a cured subgroup and informative censoring J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-11-10 Mingyue Du, Mengzhu Yu
Multivariate interval-censored failure time data occur when a failure time study involves several related failure times of interest and only interval-censored observations are available for each of...
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Complete f-moment convergence for m-asymptotic negatively associated random variables and related statistical applications J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-11-09 Xuejun Wang, Xi Chen, Tien-Chung Hu, Andrei Volodin
In this article, the complete f-moment convergence for m-asymptotic negatively associated random variables is investigated. As applications, we establish the strong consistency of the least square ...
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Persistent homology based goodness-of-fit tests for spatial tessellations J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-11-09 Christian Hirsch, Johannes Krebs, Claudia Redenbach
Motivated by the rapidly increasing relevance of virtual material design in the domain of materials science, it has become essential to assess whether topological properties of stochastic models fo...
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Approximate tolerance intervals for nonparametric regression models J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-11-02 Yafan Guo, Derek S. Young
Tolerance intervals in regression allow the user to quantify, with a specified degree of confidence, bounds for a specified proportion of the sampled population when conditioned on a set of covaria...
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Penalised estimation of partially linear additive zero-inflated Bernoulli regression models J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-10-27 Minggen Lu, Chin-Shang Li, Karla D. Wagner
We develop a practical and computationally efficient penalised estimation approach for partially linear additive models to zero-inflated binary outcome data. To facilitate estimation, B-splines are...
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A modified Nadaraya–Watson procedure for variable selection and nonparametric prediction with missing data J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-10-18 Kin Yap Cheung, Stephen M. S. Lee
We propose a new method for variable selection and prediction under a nonparametric regression setting, where a covariate may be missing either because its value is hidden from the observer or beca...
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Threshold selection for extremal index estimation J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-10-13 Natalia Markovich, Igor Rodionov
We propose a new threshold selection method for nonparametric estimation of the extremal index of stochastic processes. The discrepancy method was proposed as a data-driven smoothing tool for estim...
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Model-free prediction of time series: a nonparametric approach J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-10-11 Mohammad Mohammadi, Meng Li
We propose a novel approach for model-free time series forecasting. Unlike most existing methods, the proposed method does not rely on parametric error distributions nor assume parametric forms of ...
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On estimation of covariance function for functional data with detection limits J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-09-19 Haiyan Liu, Jeanine Houwing-Duistermaat
In many studies on disease progression, biomarkers are restricted by detection limits, hence informatively missing. Current approaches ignore the problem by just filling in the value of the detecti...
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Fighting selection bias in statistical learning: application to visual recognition from biased image databases J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-09-19 Stephan Clémençon, Pierre Laforgue, Robin Vogel
In practice, and especially when training deep neural networks, visual recognition rules are often learned based on various sources of information. On the other hand, the recent deployment of facia...
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Nonparametric relative error estimation of the regression function for left truncated and right censored time series data J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-09-02 N. Bayarassou, F. Hamrani, E. Ould Saïd
The paper introduces a nonparametric estimator for the regression function of left truncated and right censored data, achieved through minimising the mean squared relative error. Under α-mixing condition, strong uniform convergence of the estimator is established with a rate over a compact set. An extensive simulation study is conducted to assess the estimator's performance, comparing its efficiency
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Efficient nonparametric estimation of generalised autocovariances J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-09-02 Alessandra Luati, Francesca Papagni, Tommaso Proietti
This paper provides a necessary and sufficient condition for asymptotic efficiency of a nonparametric estimator of the generalised autocovariance function of a stationary random process. The genera...
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Boundary-adaptive kernel density estimation: the case of (near) uniform density J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-08-25 Jeffrey S. Racine, Qi Li, Qiaoyu Wang
We consider nonparametric kernel estimation of density functions in the bounded-support setting having known support [a,b] using a boundary-adaptive kernel function and data-driven bandwidth selec...
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Model checks for two-sample location-scale J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-08-04 Atefeh Javidialsaadi, Shoubhik Mondal, Sundarraman Subramanian
Two-sample location-scale refers to a model that permits a pair of standardised random variables to have a common base distribution. Function-based hypothesis testing in these models refers to formal tests based on distributions functions, or direct transformations thereof, that would help decide whether or not two samples come from some location-scale family of distributions. For uncensored data,
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A varying coefficient model with matrix valued covariates J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-07-24 Hong-Fan Zhang
Modern data are often collected in a matrix form. In this paper, we consider modelling the varying coefficient regression with matrix valued covariate X and scalar index variable U. The proposed model simultaneously makes principal component analysis for both the row and column dimensions of the matrix objects, maintaining the matrix structure while achieving substantial dimension reduction. We develop
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Another look at halfspace depth: flag halfspaces with applications J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-07-20 Dušan Pokorný, Petra Laketa, Stanislav Nagy
The halfspace depth is a well-studied tool of nonparametric statistics in multivariate spaces. We introduce a flag halfspace – an intermediary between a closed halfspace and its interior – and demo...
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Estimation and inference in functional varying-coefficient single-index quantile regression models J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-07-16 Hanbing Zhu, Tong Zhang, Yuanyuan Zhang, Heng Lian
We propose a flexible functional varying-coefficient single-index quantile regression model where the functional covariates of the linear part have time-varying coefficients and the single-index component offers great model flexibility in data analysis while circumventing the curse of dimensionality. The proposed model includes many existing quantile regression models for functional/longitudinal data
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The scalar-on-function modal regression for functional time series data J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-07-16 Amel Azzi, Abderrahmane Belguerna, Ali Laksaci, Mustapha Rachdi
This paper develops a new nonparametric estimator of the scalar-on function modal regression that is used to analyse the co-variability between a functional regressor and a scalar output variable. The new estimator inherits the smoothness of the kernel method and the robustness of the quantile regression. We assume that the functional observations are structured as a strong mixing functional time series
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Wasserstein filter for variable screening in binary classification in the reproducing kernel Hilbert space J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-07-16 Sanghun Jeong, Choongrak Kim, Hojin Yang
The aim of this paper is to develop a marginal screening method for variable screening in high-dimensional binary classification based on the Wasserstein distance accounting for the distributional difference. Many existing screening methods, such as the two-sample t-test and Kolmogorov test, have been developed under the parametric/nonparametric modeling assumptions to reduce the dimension of the predictors
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Detecting the complexity of a functional time series J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-07-12 Enea G. Bongiorno, Lax Chan, Aldo Goia
ABSTRACT Consider a time series that takes values in a general topological space, and suppose that its Small-ball probability is factorised into two terms that play the role of a surrogate density and a volume term. The latter allows us to study the complexity of the underlying process. In some cases, the volume term can be analytically specified in a parametric form as a function of a complexity index
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Generalized ordinal patterns in discrete-valued time series: nonparametric testing for serial dependence J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-07-10 Christian H. Weiß, Alexander Schnurr
We provide a new testing procedure to detect serial dependence in time series. Our method is based solely on the ordinal structure of the data. We explicitly allow for ties in the data windows we consider. Consequently, we use generalised ordinal patterns, that is, Cayley permutations. Unlike in the classical case, the pattern distribution is not uniform under the null hypothesis of serial independence
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Jackknife empirical likelihood for the lower-mean ratio J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-06-26 Lei Huang, Li Zhang, Yichuan Zhao
Measuring economic inequality is an important topic to explore the social system. The Gini index and Pietra ratio are used by many people but are limited to reflecting the sampling distribution. In this paper, we study the interval estimates with another measure called the lower mean ratio u. By using jackknife empirical likelihood (JEL), adjusted jackknife empirical likelihood (AJEL), mean jackknife
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Functional data analysis with rough sample paths? J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-06-23 Neda Mohammadi, Victor M. Panaretos
Functional data are typically modeled as sample paths of smooth stochastic processes in order to mitigate the fact that they are often observed discretely and noisily, occasionally irregularly and ...
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Robust oracle estimation and uncertainty quantification for possibly sparse quantiles J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-06-22 Eduard Belitser, Paulo Serra, Alexandra Vegelien
A general many quantiles + noise model is studied in the robust formulation (allowing non-normal, non-independent observations), where the identifiability requirement for the noise is formulated in...
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Model-based statistical depth with applications to functional data J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-06-21 Weilong Zhao, Zishen Xu, Yue Mu, Yun Yang, Wei Wu
ABSTRACT Statistical depth, a commonly used analytic tool in nonparametric statistics, has been extensively studied for multivariate and functional observations over the past few decades. Although various forms of depth were introduced, they are mainly procedure based whose definitions are independent of the generative model for observations. To address this problem, we introduce a generative model-based
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Estimating POT second-order parameter for bias correction J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-06-19 Nan Zou
The stable tail dependence function provides a full characterisation of the extremal dependence structures. Unfortunately, the estimation of the stable tail dependence function often suffers from significant bias, whose scale relates to the Peaks-Over-Threshold (POT) second-order parameter. For this second-order parameter, this paper introduces a penalised estimator that discourages it from being too
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An optimal sequential design in ethical allocation with an adaptive interim analysis J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-06-15 Radhakanta Das
The present article provides a distribution-free test procedure for comparing the effectiveness of two competing treatments A and B, say, in a clinical trial. Here, the relative treatment effect is measured by the functional θ=P(X
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Testing bivariate symmetry J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-06-14 Sheida Riahi, Prakash N. Patil
We consider the most basic concept of bivariate symmetry, referred to as general symmetry or conditional symmetry and provide a necessary condition for it based on the joint probability density function and the associated cumulative distribution function. This condition is then used to provide a test of general bivariate symmetry by using the sample analog of the necessary condition as a test statistic
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Improved estimation of hazard function when failure information is missing not at random J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-06-08 Feifei Chen, Wangxing Zhang, Zhihua Sun, Yuanyuan Guo
ABSTRACT Hazard function plays a crucial role in survival analysis. Its estimation has garnered a lot of attention when the survival time variable suffers from right-censoring. Most of the existing works focus on the cases that failure information is complete or missing at random (MAR). When the censoring information is missing not at random (MNAR), statistical inferences on hazard function are very
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Nonparametric inference about increasing odds rate distributions J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-06-06 Tommaso Lando, Idir Arab, Paulo Eduardo Oliveira
To improve nonparametric estimates of lifetime distributions, we propose using the increasing odds rate (IOR) model as an alternative to other popular, but more restrictive, ‘adverse ageing’ models, such as the increasing hazard rate one. This extends the scope of applicability of some methods for statistical inference under order restrictions, since the IOR model is compatible with heavy-tailed and
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On the consistency of a random forest algorithm in the presence of missing entries J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-06-06 Irving Gómez-Méndez, Emilien Joly
This paper tackles the problem of constructing a nonparametric predictor when the latent variables are given with incomplete information. The convenient predictor for this task is the random forest algorithm in conjunction to the so-called CART criterion. The proposed technique enables a partial imputation of the missing values in the data set in a way that suits both a consistent estimator of the
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Scaling by subsampling for big data, with applications to statistical learning J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-06-06 Patrice Bertail, Mohammed Bouchouia, Ons Jelassi, Jessica Tressou, Mélanie Zetlaoui
Handling large datasets and calculating complex statistics on huge datasets require important computing resources. Using subsampling methods to calculate statistics of interest on small samples is ...
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Decomposition and reproducing property of local polynomial equivalent kernels in varying coefficient models J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-05-30 Chun-Yen Wu, Li-Shan Huang, Zhezhen Jin
ABSTRACT We consider local polynomial estimation for varying coefficient models and derive corresponding equivalent kernels that provide insights into the role of smoothing on the data and fill a gap in the literature. We show that the asymptotic equivalent kernels have an explicit decomposition with three parts: the inverse of the conditional moment matrix of covariates given the smoothing variable
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Nonparametric regression with nonignorable missing covariates and outcomes using bounded inverse weighting J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-05-26 Ruoxu Tan
We consider nonparametric regression where the covariate and the outcome variable are both subject to missingness. Previous work only discussed one of the variables that may be missing, but not bot...
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Nonparametric instrument model averaging J. Nonparametr. Stat. (IF 1.2) Pub Date : 2023-05-22 Jianan Chen, Binyan Jiang, Jialiang Li
We present a new nonparametric model averaging approach to the instrumental variable (IV) regression where the effects of multiple instruments on the endogenous variable are modelled as nonparametr...