Temat: Monte Carlo Markov Chain - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Triple-goal Estimation of Unemployment Rates for U.S. States Using the U.S. Current Population Survey Data
Autorzy:: Bonnéry, Daniel
Cheng, Yang
Ha, Neung Soo
Lahiri, Partha
Powiązania:: https://bibliotekanauki.pl/articles/465991.pdf
Data publikacji:: 2015
Wydawca:: Główny Urząd Statystyczny
Tematy:: complex survey data
empirical distribution function
Monte Carlo Markov Chain
rank
risk
small area estimation
Opis:: In this paper, we first develop a triple-goal small area estimation methodology for simultaneous estimation of unemployment rates for U.S. states using the Current Population Survey (CPS) data and a two-level random sampling variance normal model. The main goal of this paper is to illustrate the utility of the triple-goal methodology in generating a single series of unemployment rate estimates for three separate purposes: developing estimates for individual small area means, producing empirical distribution function (EDF) of true small area means, and the ranking of the small areas by true small area means. We achieve our goal using a Monte Carlo simulation experiment and a real data analysis.
Źródło:: Statistics in Transition new series; 2015, 16, 4; 511-522
1234-7655
Pojawia się w:: Statistics in Transition new series
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Bayesian estimation and prediction based on Rayleigh record data with applications
Autorzy:: Awwad, Raed R. Abu
Bdair, Omar M.
Abufoudeh, Ghassan K.
Powiązania:: https://bibliotekanauki.pl/articles/1827533.pdf
Data publikacji:: 2021-09-06
Wydawca:: Główny Urząd Statystyczny
Tematy:: Bayesian estimation and prediction
Rayleigh distribution
record values
Markov Chain Monte Carlo samples
Opis:: Based on a record sample from the Rayleigh model, we consider the problem of estimating the scale and location parameters of the model and predicting the future unobserved record data. Maximum likelihood and Bayesian approaches under different loss functions are used to estimate the model's parameters. The Gibbs sampler and Metropolis-Hastings methods are used within the Bayesian procedures to draw the Markov Chain Monte Carlo (MCMC) samples, used in turn to compute the Bayes estimator and the point predictors of the future record data. Monte Carlo simulations are performed to study the behaviour and to compare methods obtained in this way. Two examples of real data have been analyzed to illustrate the procedures developed here.
Źródło:: Statistics in Transition new series; 2021, 22, 3; 59-79
1234-7655
Pojawia się w:: Statistics in Transition new series
Dostawca treści:: Biblioteka Nauki

Artykuł

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