Optimum designs for parameter estimation in a mixture experiment with two correlated responses

In this paper, we investigate a mixture problem with two responses, which are functions of the mixing proportions, and are correlated with known dispersion matrix. We obtain D- and A-optimal designs for estimating the parameters of the response functions, when none or some of the regression coefficients of the two functions are the same. It is shown that when no prior knowledge about the regression coefficients is available, the D-optimal design is independent of the dispersion matrix, while the A-optimal design depends on it, provided the response functions are of different degree. On the other hand, when some of the regression coefficients are known to be the same for both the functions, the D-optimal design depends on the dispersion matrix when the two response functions are not of the same degree.     

The exponentiated reduced Kies distribution: Properties and applications

Here we consider an exponentiated version of the reduced Kies distribution and discuss some of its properties. The parameters of the distribution are estimated by the method of maximum likelihood and illustrated with the help of certain real-life data sets. Asymptotic behavior of the maximum likelihood estimators of the parameters of the distribution is also studied by using certain simulated data sets.     

Outliers in incomplete multiresponse experiments

Identification of outlier vectors in block designs for incomplete multiresponse experiments has been considered. Design is composed of two sets of experimental units. Different numbers of response variables are observed from these two sets. Cook-statistic has been developed for identification of outliers. The developed statistic has been illustrated with a real-life data set. It has been shown that presence of outliers can distort the overall conclusion from an experiment.     

CONFLICT RESOLUTION IN INVESTMENT DECISIONS: IMPLEMENTATION OF GOAL PROGRAMMING METHODOLOGY FOR DUAL-PURPOSE FUNDS*

Goal programming (GP) is designed to resolve allocation problems with conflicting goals. Both goals and constraints are incorporated in the allocational decision, and the objective function is stated in a way that, upon solution, yields a result “as close as possible” to the priority-weighted goals. The present paper applies GP methodology to the investment decision of dual-purpose funds (DPFs), that are required by law to pursue allocational decisions with potentially conflicting objectives. It provides an empirical demonstration that DPF managers could have improved their investment selection and subsequent performance by the use of GP methodology. Finally the paper stresses the importance of sensitivity analysis to improve both the goal-ranking and target-selection aspects of the methodology and provides a limited but illuminating empirical demonstration of post-optimality analysis.     

On a characterization of the dispersion matrix based on the properties of regression

This paper provides a partial solution to a problem posed by J. Neyman (1965) regarding the characterization of multivariate negative binomial distribution based on the properties of regression. It is shown that some of the properties of regression characterize the form of the nonsingular dispersion matrix of the parent distribution, which, interestingly enough, corresponds to only two types viz. those of positive and negative multivariate binomial distributions.     

An alternative approach to variable selection for prediction

A procedure for selecting a subset of predictor variables in regression analysis is suggested. The procedure is so designed that it leads to the selection of a subset of variables having an adequate degree of informativeness with a directly specified confidence coefficient. Some examples are considered to illustrate the application of the procedure.     

Concurrent consideration of product design,process planning and production planning activities

In manufacturing engineering, product design, process planning and production planning activities are often considered independently. However, in order to effectively respond to changes in business situations, such as changes in demand forecast, product mix and technology, it is desirable to consider them concurrently. For this purpose, a large-scale linear programming model has been developed. The model considers minimization of the sum of processing cost, late shipment cost and inventory holding cost as the objective, and concurrently selects product designs, and generates process plans and production plans. The number of columns in the formulation can be large and, hence, an efficient column generation scheme is developed to solve the model. The model and solution procedure are illustrated with examples.     

On shrinkage m-estimators of location parameters

For a general class of continuous ( and marginally symmetric ) inultivariate distributions, based on suitable M-statistics ( involving bounded but possibly discontinuous score generating functions), shrinkage estimators of location are considered. These estimators are based on the James-Stein type rule and incorporates the idea of preliminary test estimation too. The main emphasis is laid on the study of asymptotic tdistributional ) risk properties of these est-innators, and asymptotic tin-) adraissibility results are also studied under fairly general regularity conditions.