The Decline of Son Preference in South Korea: The Roles of Development and Public Policy

For years, sex ratios at birth kept rising in South Korea despite rapid development. We show that this was not an anomaly: underlying son preference fell with development, but the effect of son preference on sex ratios at birth rose until the mid‐1990s as a result of improved sex‐selection technology. Now South Korea leads Asia with a declining sex ratio at birth. We explore how son preference was affected by development and by public policy. Decomposition analysis indicates that development reduced son preference primarily through triggering normative changes across society—rather than just in individuals whose socioeconomic circumstances had changed. The cultural underpinnings of son preference in preindustrial Korea were unraveled by industrialization and urbanization even as public policies sought to uphold the patriarchal family system. Our results suggest that child sex ratios in China and India may decline before those countries reach South Korean levels of development, since the governments of both countries vigorously promote normative change to reduce son preference.     

Optimum release time of a software under periodic debugging schedule

In this paper, we study the problem of finding the optimal release time of a software meant to be marketed over a finite time horizon. The optimal design scheme considers reduction of the whole cost, achievement of a specified reliability with some high probability, or a combination of the both. We assume that the software is debugged periodically and the bug detection rates in the pre-release and post-release periods are different. We derive the optimal solution in each case. We also demonstrate the utility of the results through simulations, leading to different types of graphs showing the relationship between the target reliability, specified budget and the optimal number of additional debugging periods.     

D-Optimal Designs for Covariate Parameters in Block Design Set Up

The problem considered is that of finding D-optimal design for the estimation of covariate parameters and the treatment and block contrasts in a block design set up in the presence of non stochastic controllable covariates, when N = 2(mod 4), N being the total number of observations. It is clear that when N ≠ 0 (mod 4), it is not possible to find designs attaining minimum variance for the estimated covariate parameters. Conditions for D-optimum designs for the estimation of covariate parameters were established when each of the covariates belongs to the interval [?1, 1]. Some constructions of D-optimal design have been provided for symmetric balanced incomplete block design (SBIBD) with parameters b = v, r = k = v ? 1, λ =v ? 2 when k = 2 (mod 4) and b is an odd integer.     

On D-optimal robust second order slope-rotatable designs

Das and Park (2006) introduced slope-rotatable designs overall directions for correlated observations which is known as A-optimal robust slope-rotatable designs. This article focuses D-optimal slope-rotatable designs for second-order response surface model with correlated observations. It has been established that robust second-order rotatable designs are also D-optimal robust slope-rotatable designs. A class of D-optimal robust second-order slope-rotatable designs has been derived for special correlation structures of errors.     

Geometric median and its application in the identification of multiple outliers

Geometric mean (GM) is having growing and wider applications in statistical data analysis as a measure of central tendency. It is generally believed that GM is less sensitive to outliers than the arithmetic mean (AM) but we suspect likewise the AM the GM may also suffer a huge set back in the presence of outliers, especially when multiple outliers occur in a data. So far as we know, not much work has been done on the robustness issue of GM. In quest of a simple robust measure of central tendency, we propose the geometric median (GMed) in this paper. We show that the classical GM has only 0% breakdown point while it is 50% for the proposed GMed. Numerical examples also support our claim that the proposed GMed is unaffected in the presence of multiple outliers and can maintain the highest possible 50% breakdown. Later we develop a new method for the identification of multiple outliers based on this proposed GMed. A variety of numerical examples show that the proposed method can successfully identify all potential outliers while the traditional GM fails to do so.     

Estimation in one–way classification with heteroscedastic error

For an unbalanced one way calssification under the random effect model the problem of estimation of the fixed effect parameter and the variance is considered. Tje error variance which are funtionally related to the above set of parameters are assumed to fall into k classes with constant error varaince for a class. The asymptotic properties of the proposed estimate is established for increasing number of classes k, assuming the number of observations in the classes form a fixed sequence     

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.     

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.