This paper introduces a method of blockwise empirical Euclidean likelihood for weakly dependent processes. The strong consistency andasymptotic normality of the blockwise empirical Euclidean likelihoodestimation are proved. It is deduced that the blockwise empirical Euclidean likelihood ratio statistic is asymptotically a chi-square statistic. These results show that the blockwise empirical Euclidean likelihood estimation is more asymptotically effcient than the original empirical (Euclidean) likelihood estimation and it is useful for weakly dependent processes.

In this paper, we introduce a class of generalized Browaian sheets and extend the OUP; in [1]. By studying some fundamental properties of the former process, we first derive some of simple relaxed past Markov properties of the rwo processes. Next, utilizing the relation between the two processes, we successively prove that, for two kinds of general sets, they possess the general relaxed past Markov properties and the Levy Markoy properties re-spectively. As a result, we conclude that the OUP; in [1] also has general Markov properties for the corresponding sets.

For optimal assignment of factors to columns of a 4m2n, design, we consider extensions of the minimum aberration criterion for the 2 level designs Minimum aberration 412n and 422n designs in t6 and 32 runs are obtained and tabulated Some results on the wordlength pattens and design classification are obtained.

In this paper formulas for computing the E (s2) values of some kinds of E (s2) optimal super-saturated designs are given, and a general algorithm of constructing E (s2) optimal supersaturated designs from cyclic BIBDs is proposed. Within this class of designs, by further discriminating the pairwise correlations, e cient designs of runs from 6 to 24 are constructed and tabulated. Comparisons with other existing designs are made at last, demonstrating the e ectiveness of our method.

this paper, the only assumptions on the distribution of data are those concerning rst two moments. Our purpose is to estimate the parameter of interest in the presence of nuisance parameter under these weak assumptions on the distribution. We de ne a quasi-least favorable curve andconstruct its estimator, andthen yielda pro le quasi-score function of the parameter of interest. The estimator of parameter of interest obtainedfrom this score function is asymptotically e1cient. On the other hand, we employ this method to estimate the parameter in the semiparametric model. In this model the nonparametric component plays the role of nuisance parameter andit takes values in a in nite-dimensional space. The methodis also available for semiparametric model andthe estimator obtainedby the extension is asymptotically e1cient.