Showing papers by "Indian Institute of Management Calcutta published in 1998"

Sequential estimation of normal mean under asymmetric loss function with a shrinkage stopping rule

Abstract: The problem of estimating a normal mean with unknown variance is considered under an asymmetric loss function such that the associated risk is bounded from above by a known quantity. In the absence of a fixed sample size rule, a sequential stopping rule and two sequential estimators of the mean are proposed and second-order asymptotic expansions of their risk functions are derived. It is demonstrated that the sample mean becomes asymptotically inadmissible, being dominated by a shrinkage-type estimator. Also a shrinkage factor is incorporated in the stopping rule and similar inadmissibility results are established.

Managerial Attribution and Response: An Empirical Test of an Attributional Leadership Model in India

Abstract: This study was designed to test (a) the central predictions of the attributional leadership model (S. G. Green & T. R. Mitchell, 1979) and (b) the effects of manager's leadership style and subordinate's nontask achievement on leader attribution and response. The participants were managers in India. Ratings of episodes of a subordinate's poor performance were analyzed via a 4 (leadership styles) × 2 (presence/absence of non-task achievement) factorial analysis of variance. The results supported the model's predictions: (a) there was a positive correlation between attribution and response, (b) there was a general bias toward internal attribution, and (c) leaders had a propensity to make internal responses. Significant differences in leaders' attributions and responses due to leadership style were indicated. Subordinates' nontask achievement also showed some halo effect on leaders' attributions and responses. The findings highlight the role of moderator variables and cultural differences.

Efficiency based decision rules for production planning and control

Abstract: Holt et al.'s (1960) model (the HMMS model) was one of the earliest mathematical programming approaches to production planning and control. The model uses quadratic cost functions and the coefficients of the functions are determined by curve fitting techniques from data obtained on previous operation of the plant. Regression-based approaches fit a curve which represents the average trend prevailing in the observed data, whereas, using the technique of Data Envelopment Analysis, it is possible to identify a cost efficient frontier. Estimated coefficients based on such a cost efficient frontier represent the best performance prevailing in the observed data. The two approaches for estimating the coefficients of the cost functions and the corresponding production plans are compared using a numerical example.

Estimation of a Linear Function of Multinormal Mean Vectors by Combining Available Independent Sequential Studies

Abstract: Suppose we have k independent p-variate normal populations having unknown mean vectors µi and dispersion matrices of the form σ i2Hi, where σ i2is unknown and Hi is a known p x p positive definite matrix, i = 1, 2, ... k. The investigators in these k populations may possibly have implemented different sequential or multistage sampling methodologies to estimate the mean vectors individually, having certain notions of “fixed-precision” tied with their individual problems of statistical inference. In this paper, we examine how such already available sample resources obtained from independent, but comparable studies can be fruitfully combined in order to provide a “fixed-size” confidence region for a linear combination of the k mean vectors. We discuss appropriate asymptotic second-order characteristics for the coverage probability under a fairly broad set of assumptions on the stopping variables. An example has been included involving purely sequential, accelerated sequential and three-stage sampling techniques.

On Multivariate Extension of an Inequality with a Potential Application

Abstract: Since X -m and X -m-1 are consecutive integers, the inequality (I) follows considering the expectation of the nonnegative quantity (x-m)(x-m-1). The simple inequaliy \\(1) is useful in proving nonexistence results on symmetric rothogonal arrays. For ease in reference, recall that a symmetric orthogonal array LN(sn), of strenths two, is an Nxn array, with entries from a set of s (~ 2) distinct symbols such that all the s2 possible ordered pairs of symbols appear equally often as row vectors in every N x 2 subarray thereof. In LN(sn), let x; be the number of positions in which the 1st and the ith rows have indentical entries (2 ::; i ::; N). Also, let X be an integer-valued random variable assuming values x2 , ... , XN, each with probaility l/(N -1). Then, from the definition of LN(sn), it is not hard to see that