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Toplam kayıt 19, 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 ...
Ridge-type pretest and shrinkage estimations in partially linear models
(Springer, 2020)
In this paper, we suggest pretest and shrinkage ridge regression estimators for a partially linear regression model, and compare their performance with some penalty estimators. We investigate the asymptotic properties of ...
Imputation Method Based on Sliding Window for Right-Censored Data
(Springer, 2020)
Censored data arise in almost all important statistical analyses. For example, in patient-based studies, biostatistics data often subject to right censoring due to the detection limits, or to incomplete data. In the context ...
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 ...
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 ...
Kernel Ridge Estimator for the Partially Linear Model under Right-Censored Data
(IRANIAN STATISTICAL SOC, 2021)
Objective: This paper aims to introduce a modified kernel-type ridge estimator for partially linear models under randomly-right censored data. Such models include two main issues that need to be solved: multi-collinearity ...
Comparison of parametric and semi-parametric models with randomly right-censored data by weighted estimators: Two applications in colon cancer and hepatocellular carcinoma datasets
(SAGE Publications Inc., 2021)
In this study, parametric and semi-parametric regression models are examined for random right censorship. The components of the aforementioned regression models are estimated with weights based on Cox and Kaplan-Meier ...
Right-Censored Time Series Modeling by Modified Semi-Parametric A-Spline Estimator
(MDPI, 2021)
This paper focuses on the adaptive spline (A-spline) fitting of the semiparametric regression model to time series data with right-censored observations. Typically, there are two main problems that need to be solved in ...