Practical Small-Sample Asymptotics for Regression Problems

Discrete Small Sample Asymptotics

(ABSTRACT) Random variables defined on the natural numbers may often be approximated by Poisson variables. Just as normal approximations may be improved by saddlepoint methods, Pois-son approximations may be substantially improved by tilting, expansion, and other related methods. This work will develop and examine the use of these methods, as well as present examples where such methods may be n...

Practical Small-Sample Asymptotics for Distributions Used in Life-Data Analysis

Fraser (1968) and Lawless (1982, Appendix G) discussed exact conditional intervals for the parameters and quantiles of the location-scale model when complete data are used. Moreover, Lawless (1982) extended the exact method to failure-censored (Type II censored) data. Nevertheless, the exact intervals are diicult to obtain in practice and are unavailable under time censoring (Type I censoring)....

Sample size determination for logistic regression

The problem of sample size estimation is important in medical applications, especially in cases of expensive measurements of immune biomarkers. This paper describes the problem of logistic regression analysis with the sample size determination algorithms, namely the methods of univariate statistics, logistics regression, cross-validation and Bayesian inference. The authors, treating the regr...

Asymptotics for Hazard Regression

In hazard regression (HARE), the logarithm of the conditional hazard function of a survival time given a covariate is modeled by a sum of polynomial splines and their tensor products. Under appropriate conditions, it has been shown that the (nonadaptive) HARE estimate of the conditional log-hazard function possesses an optimal L 2 rate of convergence. The current paper considers the L oo rates ...

Small-sample Forecasting Regression or Arima Models?