Class Of Estimation Procedures Research Articles

Second order asymptotic efficlency in terms of the asymptotic variance of seqrential estimation procedures in the presence of nrisance parameters

In the presence of nuisance parameters, the Bhattacharyya type bound for the asymptotic variance of estimation procedures is obtained. It is shown that the modified maximum likelihood (ML) estimation procedures together with any stopping rule does not attain the bound. Further it is shown that the modified ML estimation procedure with the appropriate stopping rule is second order asymptotically efficient in some class of estimation procedures in the sense that it attains the lower bound for the asymptotic variance in the class.

Input — Output Multipliers When Data are Incomplete or Unreliable

A general class of estimation procedures for input — output multipliers is introduced which is flexible enough to cope with all possible sets of information within the input — output framework. The class of procedures is general in the sense that multipliers derived from updated input — output tables, as well as some of the existing shortcut multiplier formulae, are included as borderline cases. Furthermore, the procedures are general in the sense that different types of multipliers (output multipliers, employment multipliers, final demand multipliers, etc) are dealt with simultaneously. It is also demonstrated how these estimation procedures could be supplemented by calculating deterministic intervals for the true but unknown results.

Inference Based on Simple Rank Step Score Statistics for the Location Model

Abstract A class of estimation procedures that are robust, relatively efficient, and yet computationally simple is proposed for the one- and two-sample location problems. Particular emphasis is placed on the problem of determining confidence intervals, a topic that has seen limited exposure in the literature. As a by-product, some interesting point estimates are obtained. A class of score functions that are ordinary step functions is considered for the location model. Point estimates and confidence intervals are obtained by inverting the corresponding rank statistics. Efficiency and robustness properties of the procedures are investigated. Simple computational schemes for the estimates and intervals are provided.