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Toplam kayıt 21, listelenen: 1-10
Censored Nonparametric Time-Series Analysis with Autoregressive Error Models
(Springer, 2020)
This paper focuses on nonparametric regression modeling of time-series observations with data irregularities, such as censoring due to a cutoff value. In general, researchers do not prefer to put up with censored cases in ...
Semiparametric modeling of the right-censored time-series based on different censorship solution techniques
(Physica-Verlag Gmbh & Co, 2020)
In this paper, we employ the penalized spline method to estimate the components of a right-censored semiparametric time-series regression model with autoregressive errors. Because of the censoring, the parameters of such ...
Estimating the Nonparametric Regression Function by Using Pade Approximation Based on Total Least Squares
(Taylor & Francis Inc, 2020)
In this paper, we propose a Pade-type approximation based on truncated total least squares (P - TTLS) and compare it with three commonly used smoothing methods: Penalized spline, Kernel smoothing and smoothing spline methods ...
Modified spline regression based on randomly right-censored data: A comparative study
(Taylor & Francis Inc, 2018)
In this paper, we propose modified spline estimators for nonparametric regression models with right-censored data, especially when the censored response observations are converted to synthetic data. Efficient implementation ...
Modified estimators in semiparametric regression models with right-censored data
(Taylor & Francis Ltd, 2018)
In this work we introduce different modified estimators for the vector parameter and an unknown regression function g in semiparametric regression models when censored response observations are replaced with synthetic data ...
Optimum shrinkage parameter selection for ridge type estimator of Tobit model
(Taylor and Francis Ltd., 2020)
This paper presents different ridge type estimators based on maximum likelihood ((Formula presented.)) for parameters of a Tobit model. In this context, an algorithm is introduced to get the estimators based on (Formula ...
Truncation level selection in nonparametric regression using Padé approximation
(Taylor and Francis Inc., 2019)
This paper introduces a Padé-type approximation for an unknown regression function in a nonparametric regression model. This newly introduced approximation provides a linear model with multi-collinearities and errors in ...
Choice of smoothing parameter for kernel type ridge estimators in semiparametric regression models
(National Statistical Institute, 2021)
This paper concerns kernel-type ridge estimators of parameters in a semiparametric model. These estimators are a generalization of the well-known Speckman’s approach based on kernel smoothing method. The most important ...
Bandwidth Selection Problem for Nonparametric Regression Model with Right-Censored Data
(Natl Inst Statistics, 2017)
In this paper, the proposed estimator for the unknown nonparametric regression function is a Nadarya-Watson (Nadarya, 1964; Watson, 1964) type kernel estimator. In this estimation procedure, the censored observations are ...
Smoothing parameter selection in semiparametric regression models with censored data
(Inst Advanced Science Extension, 2017)
In this paper, we introduce penalized spline estimators for the unknown function and a parameter vector in a semiparametric regression model with right censored data. In order to obtain this estimator accurately and ...