# Statistical Methods & Applications

Statistical Methods & Applications > 1997 > 6 > 1 > 23-35

Statistical Methods & Applications > 1997 > 6 > 1 > 59-65

Statistical Methods & Applications > 1997 > 6 > 1 > 1-21

*k*competing risks when the random variables of interest (risks) are censored from the left with the unobservable random variable. The nonparametrical estimators for survival functions of risks are presented and the estimators are strongly approximated with the best rates by appropriate Gaussian processes.

Statistical Methods & Applications > 1997 > 6 > 1 > 67-81

*k*tables when there is uncertain prior information about homogeneity constraint on them. Using a preliminary test approach, we propose seven estimators and study their properties of asymptotic dominance under local alternatives. In the process, we propose a Wald-type test statistic for testing homogeneity and obtain its...

Statistical Methods & Applications > 1997 > 6 > 1 > 83-92

Statistical Methods & Applications > 1997 > 6 > 1 > 93-95

Statistical Methods & Applications > 1997 > 6 > 1 > 37-58

Statistical Methods & Applications > 1997 > 6 > 2 > 161-176

Statistical Methods & Applications > 1997 > 6 > 2 > 97-130

Statistical Methods & Applications > 1997 > 6 > 2 > 131-138

Statistical Methods & Applications > 1997 > 6 > 2 > 177-199

Statistical Methods & Applications > 1997 > 6 > 2 > 139-141

Statistical Methods & Applications > 1997 > 6 > 2 > 145-159

*projection*estimators tend to overfit the density if the number of basis functions in the orthogonal expansion is too large. In extreme cases the estimator is close to the Dirac function concentrated at the observations. We propose a roughness...

Statistical Methods & Applications > 1997 > 6 > 3 > 213-231

Statistical Methods & Applications > 1997 > 6 > 3 > 233-243

Statistical Methods & Applications > 1997 > 6 > 3 > 245-255

*p*and

*q*, we derive parametric conditions which guarantee the identification of Generalized STARMA models.

Statistical Methods & Applications > 1997 > 6 > 3 > 257-272

Statistical Methods & Applications > 1997 > 6 > 3 > 201-211

Statistical Methods & Applications > 1997 > 6 > 3 > 273-284

*LBE*) for incorrect prior assumptions under the misspecified linear regression model. In order to obtain the mean square error (

*MSE*)-matrix properties of the

*LBE*, we compare the

*MSE-matrix*of the

*LBE*averaging over the correct prior assumptions with that averaging over incorrect prior assumptions. We also compare the

*MSE-matrices*of the ordinary...