Results 21 to 30 of about 363 (160)

Bayesian Estimation of Gumbel Type-II Distribution

Data Science Journal, 2013
In this paper we consider the Bayesian estimators for the unknown parameters of Gumbel type-II distribution. The Bayesian estimators cannot be obtained in closed forms.
Kamran Abbas, Jiayu Fu, Yincai Tang
doaj   +1 more source

Bayesian Estimation of Student-t GARCH Model Using Lindley’s Approximation

ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, 2019
The dependency of conditional second moments of financial time series is modelled by Generalized Autoregressive conditionally heteroscedastic (GARCH) processes. The maximum likelihood estimation (MLE) procedure is most commonly used for estimating the unknown parameters of a GARCH model.
Arı, Yakup, Papadopoulos, Alex
openaire   +1 more source

Bayesian Analysis of Generalized Exponential Distribution [PDF]

, 2016
Bayesian estimators of unknown parameters of a two parameter generalized exponential distribution are obtained based on non-informative priors using different loss ...
Ahmad, S. P., Ahmed, Aquil, Naqash, Saima   +2 more
core   +2 more sources

Lindely’s method to estimate the parameters of the univariate truncated t Regression Model using informative prior information [PDF]

, 2021
   In this paper, the parameters of the truncated t-regression model were estimated, in which the response variable follows a two-sided truncated t-distribution, and its parameters were estimated by an approximate Bayesian technique according ...
Hussain, Elham Abdulkreem
core   +2 more sources

Bayesian inference on reliability parameter with non-identical-component strengths for Rayleigh distribution [PDF]

Journal of Mahani Mathematical Research
In this paper, we delve into Bayesian inference related to multi-component stress-strength parameters, focusing on non-identical component strengths within a two-parameter Rayleigh distribution under the progressive first failure censoring scheme.
Akram Kohansal
doaj   +1 more source

Estimation of Generalized Gompertz Distribution Parameters under Ranked-Set Sampling

Journal of Probability and Statistics, 2020
This paper studies estimation of the parameters of the generalized Gompertz distribution based on ranked-set sample (RSS). Maximum likelihood (ML) and Bayesian approaches are considered.
Mohammed Obeidat, Amjad Al-Nasser, Amer I. Al-Omari   +2 more
doaj   +1 more source

Classical and Bayesian Estimation of the Inverse Weibull Distribution: Using Progressive Type-I Censoring Scheme

Advances in Civil Engineering, 2021
The challenge of estimating the parameters for the inverse Weibull (IW) distribution employing progressive censoring Type-I (PCTI) will be addressed in this study using Bayesian and non-Bayesian procedures.
Ali Algarni, Mohammed Elgarhy, Abdullah M Almarashi, Aisha Fayomi, Ahmed R El-Saeed   +4 more
doaj   +1 more source

Bayesian Estimation Using Product of Spacing for Modified Kies Exponential Progressively Censored Data

Axioms, 2023
In life testing and reliability studies, most researchers have used the maximum likelihood estimation method to estimate unknown parameters, even though it has been proven that the maximum product of spacing method has properties as good as the maximum ...
Talal Kurdi, Mazen Nassar, Farouq Mohammad A. Alam   +2 more
doaj   +1 more source

Inequalities and Approximations of Weighted Distributions by Lindley Reliability Measures, and the Lindley-Cox Model with Applications

International Journal of Statistics and Probability, 2015
In this note, stochastic comparisons and results for weighted and Lindley models are presented. Approximation of weighted distributions via Lindley distribution in the class of increasing failure rate (IFR) and decreasing failure rate (DFR) weighted distributions with monotone weight functions are obtained including approximations via the length-biased
Oluyede, Broderick O., Okwuokenye, Macaulay, Peace, Karl E.   +2 more
openaire   +3 more sources

Particle filtering and parameter learning [PDF]

, 2007
This paper provides a new approach for sequentially learning parameters and states in a wide class of state space models using particle filters. Our approach generates direct i.i.d.
Michael Johannes, Nicholas Polson
core   +1 more source