Empirical Hierarchical Bayes Estimation

1992 ◽  
pp. 147-161
Author(s):  
C. G. Khatri ◽  
C. Radhakrishna Rao
Keyword(s):  
Hierarchical Bayes ◽  
Bayes Estimation

Robust hierarchical Bayes estimation of exchangeable means

1991 ◽  
Vol 19 (1) ◽  
pp. 39-56 ◽  
Author(s):  
Jean-François Angers ◽  
James O. Berger
Keyword(s):  
Hierarchical Bayes ◽  
Bayes Estimation

scholarly journals Spatial hierarchical Bayes estimation of mean years of schooling

2018 ◽  
Vol 300 ◽  
pp. 012031
Author(s):  
Dwi A S Wahyuni ◽  
Sutarman Wage ◽  
Open Darnius
Keyword(s):  
Hierarchical Bayes ◽  
Bayes Estimation ◽  
Years Of Schooling

Adaptive Hierarchical Bayes Estimation of Small Area Proportions

2017 ◽  
Vol 69 (2) ◽  
pp. 150-164 ◽  
Author(s):  
Benmei Liu ◽  
Partha Lahiri
Keyword(s):  
Random Effects ◽  
Small Area ◽  
Probability Distributions ◽  
Random Effect ◽  
Real Data ◽  
Hierarchical Bayes ◽  
Bayes Estimation ◽  
Proposed Model ◽  
Exponential Power ◽  
Power Class

Unit-level logistic regression models with mixed effects have been used for estimating small area proportions in the literature. Normality is commonly assumed for the random effects. Nonetheless, real data often show significant departures from normality assumptions of the random effects. To reduce the risk of model misspecification, we propose an adaptive hierarchical Bayes estimation approach in which the distribution of the random effect is chosen adaptively from the exponential power class of probability distributions. The richness of the exponential power class ensures the robustness of our hierarchical Bayes approach against departure from normality. We demonstrate the robustness of our proposed model using both simulated and real data. The results suggest that the proposed model works reasonably well to incorporate potential kurtosis of the random effects distribution.

scholarly journals PENAKSIRAN NILAI UTILITAS PADA ANALISIS KONJOIN DENGAN METODE HIERARCHICAL BAYES

2019 ◽  
Vol 1 (01) ◽  
pp. 54
Author(s):  
Idfi Setyaningrum
Keyword(s):  
Conjoint Analysis ◽  
Hierarchical Bayes ◽  
Bayes Estimation ◽  
Individual Part ◽  
Combining Data ◽  
Do So

Most methods for analyzing conjoint do so by combining data for all individuals. There could be extreme weakness in analyzing data this way, for it could obscure important individual aspects of the data. Hierarchical Bayes Estimation is one method of estimating individual part-worths. This method can reasonably estimate individual part-worths even with relatively little data from each respondent. In this paper we provide an introduction to Hierarchical Bayes Estimation and use this algorithm written in WinBUGS 1.4. to perform a conjoint analysis.

scholarly journals All aboard the corporate socially and environmentally responsible cruise ship

2017 ◽  
Vol 9 (1) ◽  
pp. 31-43 ◽  
Author(s):  
Sheree-Ann Adams ◽  
Xavier Font ◽  
Davina Stanford
Keyword(s):  
Conjoint Analysis ◽  
Consumer Preferences ◽  
Hierarchical Bayes ◽  
Lifestyle Changes ◽  
Bayes Estimation ◽  
Content Type ◽  
Price Duration ◽  
First Time ◽  
Sawtooth Software ◽  
Policies And Practices

Purpose The purpose of the study was to examine the relative importance of corporate social and environmental responsibility (CSER) in comparison to standard, price, duration, destination, brand and disruption using choice-based conjoint analysis (CBC). Design/methodology/approach CBC was used as the data collection survey technique, and counts analysis for preference and hierarchical Bayes estimation (HB) for importance levels data analysis methods, from Sawtooth Software Inc. Findings Results show that 2:1 Royal Caribbean Cruise Line cruise consumers prefer companies with CSER policies and practices. However, their actual product choice selection of cruise package attributes revealed that consumers overall placed less importance on CSER when choosing cruises. Experienced consumers were more brand image-conscious than those new to cruising, and consumers who were less price-sensitive were most willing to choose companies with CSER policies and practices. Research limitations/implications The information provided is specifically on “what” cruise consumer preferences and importance attributes are but does not explicitly explain “why” the respondents made the choices they did. This was at the time a limitation of the software used to conduct the study. Practical implications The Conjoint Analysis CBC Sawtooth Software pre-2014 version choice simulators do not facilitate questions that provide answers as to “why” respondents make the choices they do in the market simulations. Social implications The knowledge contribution is of value to both academia and industry, as the quantitative statistical data on the cruise consumers’ choice preferences are of value in understanding and identifying solutions/approaches towards “opening the bottleneck” that exists between private sector sustainable development practices and consumer lifestyle changes. Originality/value This was the first time that CBC/HB was applied within academia to examine the cruise consumers’ choice preferences in a UK context and also the first time that CSER was applied as a direct variable in a cruise package to determine the preference and important values of a brand in a consumer behaviour decision-making context.

Detecting a Change in School Performance: A Bayesian Analysis for a Multilevel Join Point Problem

2001 ◽  
Vol 26 (4) ◽  
pp. 443-468 ◽  
Author(s):  
Yeow Meng Thum ◽  
Suman K. Bhattacharya
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Test Scores ◽  
Evaluation Studies ◽  
Regression Function ◽  
Hierarchical Bayes ◽  
Bayes Estimation ◽  
Point Problem ◽  
Two Phase ◽  
Multilevel Linear Regression

A substantial literature on switches in linear regression functions considers situations in which the regression function is discontinuous at an unknown value of the regressor, Xk , where k is the so-called unknown “change point.” The regression model is thus a two-phase composite of yi ∼ N(β01 + β11xi, σ12), i=1, 2,..., k and yi ∼ N(β02 + β12xi, σ22), i= k + 1, k + 2,..., n. Solutions to this single series problem are considerably more complex when we consider a wrinkle frequently encountered in evaluation studies of system interventions, in that a system typically comprises multiple members (j = 1, 2, . . . , m ) and that members of the system cannot all be expected to change synchronously. For example, schools differ not only in whether a program, implemented system-wide, improves their students’ test scores, but depending on the resources already in place, schools may also differ in when they start to show effects of the program. If ignored, heterogeneity among schools in when the program takes initial effect undermines any program evaluation that assumes that change points are known and that they are the same for all schools. To describe individual behavior within a system better, and using a sample of longitudinal test scores from a large urban school system, we consider hierarchical Bayes estimation of a multilevel linear regression model in which each individual regression slope of test score on time switches at some unknown point in time, kj. We further explore additional results employing models that accommodate case weights and shorter time series.

E-Bayes Estimation of Rayleigh Distribution Parameter

2014 ◽  
Vol 596 ◽  
pp. 305-308 ◽  
Author(s):  
Huan Bin Liu ◽  
Tong Yin ◽  
Cheng Wang
Keyword(s):  
Numerical Simulation ◽  
Loss Function ◽  
Beta Distribution ◽  
Prior Distribution ◽  
Rayleigh Distribution ◽  
Hierarchical Bayes ◽  
Distribution Parameter ◽  
Bayes Estimation ◽  
Entropy Loss

This paper provided the E-Bayes estimation and hierarchical Bayes estimation of Rayleigh distribution parameter under q-symmetric entropy loss function, when the prior distribution is Beta distribution, and the relation between E-Bayes estimation and hierarchical Bayes estimation was discussed. Numerical simulation shows that the results of these two types of estimation are close, and E-Bayes estimation is more convenient in terms of calculation complexity.

Sign in / Sign up

Export Citation Format

Share Document