Estimation and Reconstruction Based on Left Censored Data from Pareto Model

نویسندگان

چکیده مقاله:

In this paper, based on a left censored data from the twoparameter Pareto distribution, maximum likelihood and Bayes estimators for the two unknown parameters are obtained. The problem of reconstruction of the past failure times, either point or interval, in the left-censored set-up, is also considered from Bayesian and non-Bayesian approaches. Two numerical examples and a Monte Carlo simulation study are given for illustrative purposes.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Estimation Based on Progressively Censored Data from the Burr Model

Based on progressively Type-II censored samples, the uniformly minimum variance unbiased (UMVU), Bayes and empirical Bayes estimates for the unknown parameter and the reliability function of the Burr model are derived. The Bayes and empirical Bayes estimates are obtained based on absolute error and logarithmic loss functions. We also present a numerical example and a Monte Carlo simulation stud...

متن کامل

Small-Area Estimation based on Survey Data from a Left-Censored Fay-Herriot Model

We study Small Area Estimation based on data obtained by leftcensored responses from a Fay-Herriot (1979) normal-error model. The problem is motivated by the Census Bureau’s ongoing Small Area Income and Poverty Estimation (SAIPE) project, where a FH model is fitted to a logarithmically transformed response variable (count of sampled poor children within a CPS-sampled county), with PSU’s provid...

متن کامل

Estimation of Parameters for an Extended Generalized Half Logistic Distribution Based on Complete and Censored Data

This paper considers an Extended Generalized Half Logistic distribution. We derive some properties of this distribution and then we discuss estimation of the distribution parameters by the methods of moments, maximum likelihood and the new method of minimum spacing distance estimator based on complete data. Also, maximum likelihood equations for estimating the parameters based on Type-I and Typ...

متن کامل

Classic and Bayes Shrinkage Estimation in Rayleigh Distribution Using a Point Guess Based on Censored Data

Introduction      In classical methods of statistics, the parameter of interest is estimated based on a random sample using natural estimators such as maximum likelihood or unbiased estimators (sample information). In practice,  the researcher has a prior information about the parameter in the form of a point guess value. Information in the guess value is called as nonsample information. Thomp...

متن کامل

Quantile Estimation for Left Truncated and Right Censored Data

In this paper we study the estimation of a quantile function based on left truncated and right censored data by the kernel smoothing method. Asymptotic normality and a Berry-Esseen type bound for the kernel quantile estimator are derived. Monte Carlo studies are conducted to compare the proposed estimator with the PL-quantile estimator.

متن کامل

Comparison of three Estimation Procedures for Weibull Distribution based on Progressive Type II Right Censored Data

In this paper, based on the progressive type II right censored data, we consider estimates of MLE and AMLE of scale and shape parameters of weibull distribution. Also a new type of parameter estimation, named inverse estimation, is introdued for both shape and scale parameters of weibull distribution which is used from order statistics properties in it. We use simulations and study the biases a...

متن کامل

منابع من

با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ذخیره در منابع من قبلا به منابع من ذحیره شده

{@ msg_add @}


عنوان ژورنال

دوره 13  شماره None

صفحات  151- 175

تاریخ انتشار 2014-12

با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.

کلمات کلیدی

میزبانی شده توسط پلتفرم ابری doprax.com

copyright © 2015-2023