Small Sample Bias in the Gamma Frailty Model for Univariate Survival

2005 ◽  
Vol 11 (2) ◽  
pp. 265-284 ◽  
Author(s):  
Peter Barker ◽  
Robin Henderson
Keyword(s):  
Frailty Model ◽  
Small Sample ◽  
Sample Bias ◽  
Gamma Frailty

scholarly journals Asymptotic theory for the correlated gamma-frailty model

1998 ◽  
Vol 26 (1) ◽  
pp. 183-214 ◽  
Author(s):  
Erik Parner
Keyword(s):  
Asymptotic Theory ◽  
Frailty Model ◽  
Gamma Frailty

scholarly journals Gamma frailty model for linkage analysis with application to interval-censored migraine data

Biostatistics ◽  
2008 ◽  
Vol 10 (1) ◽  
pp. 187-200 ◽  
Author(s):  
M. A. Jonker ◽  
S. Bhulai ◽  
D. I. Boomsma ◽  
R. S. L. Ligthart ◽  
D. Posthuma ◽  
...  
Keyword(s):  
Linkage Analysis ◽  
Frailty Model ◽  
Gamma Frailty ◽  
Interval Censored

Small-Sample Bias of Point Estimators of the Odds Ratio from Matched Sets

Biometrics ◽  
1984 ◽  
Vol 40 (2) ◽  
pp. 421 ◽  
Author(s):  
Nicholas P. Jewell
Keyword(s):  
Odds Ratio ◽  
Small Sample ◽  
Sample Bias ◽  
Point Estimators

A Mixture Shared Gamma Frailty Model Under Gompertz Baseline Distribution

2020 ◽  
Vol 21 (1) ◽  
pp. 187-200
Author(s):  
Arvind Pandey ◽  
Shashi Bhushan ◽  
Lalpawimawha Ralte
Keyword(s):  
Frailty Model ◽  
Gamma Frailty

An exact iterated bootstrap algorithm for small-sample bias reduction

2001 ◽  
Vol 36 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Kenny Y.F. Chan ◽  
Stephen M.S. Lee
Keyword(s):  
Bias Reduction ◽  
Small Sample ◽  
Sample Bias ◽  
Bootstrap Algorithm

scholarly journals Dynamic Regression and Filtered Data Series: A Laplace Approximation to the Effects of Filtering in Small Samples

1996 ◽  
Vol 12 (3) ◽  
pp. 432-457 ◽  
Author(s):  
Eric Ghysels ◽  
Offer Lieberman
Keyword(s):  
Regression Model ◽  
Quadratic Forms ◽  
Mean Squared Error ◽  
Small Sample ◽  
Data Series ◽  
Finite Sample ◽  
Sample Bias ◽  
Squared Error ◽  
Lagged Dependent Variable ◽  
Dynamic Regression Model

It is common for an applied researcher to use filtered data, like seasonally adjusted series, for instance, to estimate the parameters of a dynamic regression model. In this paper, we study the effect of (linear) filters on the distribution of parameters of a dynamic regression model with a lagged dependent variable and a set of exogenous regressors. So far, only asymptotic results are available. Our main interest is to investigate the effect of filtering on the small sample bias and mean squared error. In general, these results entail a numerical integration of derivatives of the joint moment generating function of two quadratic forms in normal variables. The computation of these integrals is quite involved. However, we take advantage of the Laplace approximations to the bias and mean squared error, which substantially reduce the computational burden, as they yield relatively simple analytic expressions. We obtain analytic formulae for approximating the effect of filtering on the finite sample bias and mean squared error. We evaluate the adequacy of the approximations by comparison with Monte Carlo simulations, using the Census X-11 filter as a specific example

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