On pitman nearness and variance of estimators

In this article, some conditions on variances are presented under which the (Generalized) Pitman Nearness Criterion Would Prefer one estimator over another. Results for univariate as well as multivariate cases are derived. An exact expression for a result of Rao, Keating and Mason (1985) is provided.     

Discrepancy in regression estimates between log-normal and gamma: some case studies

In regression models with multiplicative error, estimation is often based on either the log-normal or the gamma model. It is well known that the gamma model with constant coefficient of variation and the log-normal model with constant variance give almost the same analysis. This article focuses on the discrepancies of the regression estimates between the two models based on real examples. It identifies that even though the variance or the coefficient of variation remains constant, but regression estimates may be different between the two models. It also identifies that for the same positive data set, the variance is constant under the log-normal model but non-constant under the gamma model. For this data set, the regression estimates are completely different between the two models. In the process, it explains the causes of discrepancies between the two models.     

Estimating the parameter of selected uniform population under the squared log error loss function

Let π₁, …, π_k be k (? 2) independent populations, where π_i denotes the uniform distribution over the interval (0, θ_i) and θ_i > 0 (i = 1, …, k) is an unknown scale parameter. The population associated with the largest scale parameter is called the best population. For selecting the best population, We use a selection rule based on the natural estimators of θ_i, i = 1, …, k, for the case of unequal sample sizes. Consider the problem of estimating the scale parameter θ_L of the selected uniform population when sample sizes are unequal and the loss is measured by the squared log error (SLE) loss function. We derive the uniformly minimum risk unbiased (UMRU) estimator of θ_L under the SLE loss function and two natural estimators of θ_L are also studied. For k = 2, we derive a sufficient condition for inadmissibility of an estimator of θ_L. Using these condition, we conclude that the UMRU estimator and natural estimator are inadmissible. Finally, the risk functions of various competing estimators of θ_L are compared through simulation.     

A UNIFIED APPROACH TO THE PRICE-BREAK ECONOMIC ORDER QUANTITY (EOQ) PROBLEM*

In many developing countries where supply is limited, a premium rather than a discounted price is paid for buying in larger amounts. In order for management science/operations research solutions to be relevant to developing economies, such realities need to be explicitly recognized and any decisions evaluated from that perspective. To illustrate this point, a typical inventory-purchasing problem is solved. In the process, formulae are developed that introduce simultaneous price and quantity sensitivity into the inventory equation. A systematic procedure is suggested to locate the optimal order quantity for a given schedule of discounts or premiums.     

Are social movements truly social? The prosocial and antisocial outcomes of social movements

A growing body of research demonstrates that U.S. politics has become increasingly polarized over the past few decades. In these polarized times, what potential roles might social movements play in bridging divides between, or perhaps further dividing, people across a variety of political and social groups? In this article, we propose a research agenda for social movement studies focused on the prosocial and antisocial outcomes of social movements. Although scholars commonly frame their work on the consequences of social movements in terms of social movements' political, economic, cultural, and biographical outcomes, we suggest a focus on two categories of social movement outcomes (prosocial and antisocial outcomes) that cut across prior theoretical categories, and we show how an emerging body of scholarship has documented such outcomes at micro, meso, and macro levels of analysis. We also consider how emerging scholarship has addressed the sociological question about the conditions under which social movements produce prosocial versus antisocial outcomes. As we argue, attention to prosocial and antisocial outcomes of social movements holds both theoretical implications for social movement research and practical implications for social movements navigating the United States' political and social divides.     

Determinants of Individual Giving Behavior in Urban India

This paper, based on a primary sample survey over 1925 earning individuals in the cities of Kolkata, Cuttack and Bengaluru, examines how the individual and household characteristics influence the acts of giving in urban India. The regression results indicate income, family size and property ownership affecting likelihood and extent of giving. Likelihood to give is more with females, though males tend to donate more. There exists threshold income beyond which likelihood to donate is less. Characteristics like age, education, dependency ratio and marital status influence certain acts of giving. As the opportunity cost of non-cash giving increases with the rise in income, cash donations substitute non-cash giving. There also prevails complementarity in the acts of giving. On behavioral front, in addition to work–life balance and pledging, the notion of rational choice seems to be gaining ground.

     

On weighted generalized entropy

In the literature of information theory, the concept of generalized entropy has been proposed and the length-based shift dependent information measure has been studied. In this paper, the concept of weighted generalized entropy has been introduced. The properties of weighted generalized residual entropy and weighted generalized past entropy are also discussed.     

On generalized geometric distributions and improved estimation of batting average in cricket

Batting average is the most popular way of measuring a batsman's performance in cricket. However, in light of scores from not-out innings, the conventional way of computing the batting average is unsatisfactory from theoretical statistical perspective, as well as from intuitive and practitioner's point of view. We adopt alternative methods of calculating batting average, treating not-outs as right-censored data and using generalized class of geometric distributions (GGD) as models for the runs scored. In the proposed family of GGD, the generalization lies in the hazard of getting out possibly changing from one score to another. Each postulated structure of the hazards leads to a different member of the GGD family. Selection of appropriate member from the GGD family and maximum likelihood estimation of the hazard parameters in the model are discussed theoretically with illustrations. The proposed method subsumes the traditional average and product limit (Kaplan-Meier) estimate as the two extreme scenarios within this structure. We also discuss two alternative methods of estimating the true mean under the proposed framework and deliberate on issues while adopting these practices in practice.     

Mixture designs with orthogonal blocks

Constructions of blocked mixture designs are considered in situations where BLUEs of the block effect contrasts are orthogonal to the BLUEs of the regression coefficients. Orthogonal arrays (OA), Balanced Arrays (BAs), incidence matrices of balanced incomplete block designs (BIBDs), and partially balanced incomplete block designs (PBIBDs) are used. Designs with equal and unequal block sizes are considered. Also both cases where the constants involved in the orthogonality conditions depend and do not depend on the factors have been taken into account. Some standard (already available) designs can be obtained as particular cases of the designs proposed here.