scholarly journals Standard Error of Empirical Bayes Estimate in NONMEM® VI

2012 ◽  
Vol 16 (2) ◽  
pp. 97 ◽  
Author(s):  
Dongwoo Kang ◽  
Kyun-Seop Bae ◽  
Brett E. Houk ◽  
Radojka M. Savic ◽  
Mats O. Karlsson
Keyword(s):  
Standard Error ◽  
Empirical Bayes ◽  
Bayes Estimate ◽  
Empirical Bayes Estimate

An Empirical Bayes Estimate of Market Risk

1982 ◽  
Vol 28 (7) ◽  
pp. 728-737 ◽  
Author(s):  
Steven F. Maier ◽  
David W. Peterson ◽  
James H. Vander Weide
Keyword(s):  
Empirical Bayes ◽  
Market Risk ◽  
Bayes Estimate ◽  
Empirical Bayes Estimate

Empirical Bayes Estimation of Parameter of Burr-Type X Model under LINEX and Squared Error Loss Functions

2011 ◽  
Vol 403-408 ◽  
pp. 5273-5277
Author(s):  
Hai Ying Lan
Keyword(s):  
Empirical Bayes ◽  
Bayes Estimation ◽  
Loss Functions ◽  
Bayes Estimate ◽  
Squared Error Loss ◽  
Bayes Estimates ◽  
Error Loss ◽  
Squared Error ◽  
Linex Loss ◽  
Empirical Bayes Estimate

The Empirical Bayes estimate of the parameter of Burr-type X distribution is contained .The estimate is obtained under squared error loss and Varian’s linear-exponential (LINEX) loss functions, and is compared with corresponding maximum likelihood and Bayes estimates. Finally, a Monte Carlo numerical example is given to illustrate our results.

scholarly journals Bayes Shrinkage Estimation of the Parameter of Rayleigh Distribution for Progressive Type-II Censored Data

2015 ◽  
Vol 44 (4) ◽  
pp. 3-15 ◽  
Author(s):  
Sanku Dey ◽  
Tanujit Dey ◽  
Sudhansu S. Maiti
Keyword(s):  
Censored Data ◽  
Empirical Bayes ◽  
Risk Function ◽  
Rayleigh Distribution ◽  
Shrinkage Estimation ◽  
Type Ii ◽  
Bayes Estimate ◽  
Data Set ◽  
Progressive Type ◽  
Type Ii Censored Data

This paper derives Bayes shrinkage estimator of Rayleigh parameter and its associated risk based on conjugate prior under the assumption of general entropy loss function for progressive type-II censored data. Risk function of maximum likelihood estimate, Bayes estimate and Bayes shrinkage estimate have also been derived and compared. A procedure has been suggested to include a guess value in case of the Bayes shrinkage estimation. Risk function of empirical Bayes estimate and empirical Bayes shrinkage estimate have also been derived and compared. In conclusion, an illustrative example is presented to assess how the Rayleigh distribution fits a real data set.

Enhanced Methodology for the Identification of Locations with High Risk of Wet Crashes

2019 ◽  
Vol 2673 (11) ◽  
pp. 29-37
Author(s):  
Kenneth X. Vélez Rodríguez ◽  
Samer W. Katicha ◽  
Gerardo W. Flintsch
Keyword(s):  
Empirical Bayes ◽  
Negative Binomial ◽  
Water Film ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Limited Attention ◽  
Expected Number ◽  
Binomial Regression ◽  
Empirical Bayes Estimate ◽  
Grade Difference

About 18% of crashes on Virginia’s interstates from 2014 to 2016 were reported to be wet crashes. Although extensive research on crashes has been conducted, limited attention has been devoted to the prediction of wet crashes. The ratio of wet over dry crashes (wet over dry ratio [WDR]) has traditionally been the parameter of interest. In this paper, negative binomial regression is used to quantify the relationship between WDR and traffic and road parameters. One issue with the WDR is the handling of sites with zero dry crash counts. This was addressed by numerically replacing the zeros with 0.5 or by using an empirical Bayes estimate of the expected number of dry crashes instead of the dry crash counts. The empirical Bayes approach resulted in a better model fit as measured using Akaike’s Information Criterion. The negative binomial model developed for wet crashes was used to identify parameters that affect the pavement water film thickness and the expected number of wet crashes. The approach identified the longitudinal grade difference as an important parameter.

The combined Herstmonceux Cambridge Program of optical positions of radio sources

1978 ◽  
Vol 48 ◽  
pp. 155-166 ◽  
Author(s):  
A. N. Argue ◽  
E. D. Clements ◽  
G. M. Harvey ◽  
C. A. Murray
Keyword(s):  
Standard Error ◽  
Unit Weight ◽  
Radio Sources

SummaryAGK3-based optical positions are presented for 38 counterparts of radio sources selected from the catalogue of Elsmore & Ryle. The measurements were made from plates taken with the 13-inch Astrograph, the 26-inch refractor and the 2.5 m (INT) reflector at Herstmonceux, and the 17-inch Schmidt at Cambridge. The standard error for a mean position of unit weight is 0”.11, and the weights range from 3.0 for the brightest sources to 0.5 for the faintest. Comparison with the radio positions shows no significant differences. The effects of applying the Brorfelde corrections to AGK3 are discussed.

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