Estimation with right-censored observations under a semi-Markov model
نویسندگان
چکیده
منابع مشابه
Estimation with Right-Censored Observations Under A Semi-Markov Model.
The semi-Markov process often provides a better framework than the classical Markov process for the analysis of events with multiple states. The purpose of this paper is twofold. First, we show that in the presence of right censoring, when the right end-point of the support of the censoring time is strictly less than the right end-point of the support of the semi-Markov kernel, the transition p...
متن کاملNonparametric Demand Forecasting with Right Censored Observations
In a newsvendor inventory system, demand observations often get right censored when there are lost sales and no backordering. Demands for newsvendor-type products are often forecasted from censored observations. The Kaplan-Meier product limit estimator is the well-known nonparametric method to deal with censored data, but it is undefined beyond the largest observation if it is censored. To addr...
متن کاملEstimation in a semi-Markov transformation model.
Semi-Markov and modulated renewal processes provide a large class of multi-state models which can be used for analysis of longitudinal failure time data. In biomedical applications, models of this kind are often used to describe evolution of a disease and assume that patient may move among a finite number of states representing different phases in the disease progression. Several authors propos...
متن کاملShrinkage Preliminary Test Estimation under a Precautionary Loss Function with Applications on Records and Censored Ddata
Shrinkage preliminary test estimation in exponential distribution under a precautionary loss function is considered. The minimum risk-unbiased estimator is derived and some shrinkage preliminary test estimators are proposed. We apply our results on censored data and records. The relative efficiencies of proposed estimators with respect to the minimum ‎risk-unbiased‎&...
متن کاملKernel Ridge Estimator for the Partially Linear Model under Right-Censored Data
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 and censorship. To address these issues, we improved the kernel estimator based on synthetic data transformation and kNN imputation techniques. The key idea of this paper is t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Canadian Journal of Statistics
سال: 2013
ISSN: 0319-5724
DOI: 10.1002/cjs.11176