Dimensions of women’s autonomy and the influence on maternal health care utilization in a north indian city

The dimensions of women's autonomy and their relationship to maternal health care utilization were investigated in a probability sample of 300 women in Varanasi, India. We examined the determinants of women's autonomy in three areas: control over finances, decision-making power, and freedom of movement. After we control for age, education, household structure, and other factors, women with closer ties to natal kin were more likely to have greater autonomy in each of these three areas. Further analyses demonstrated that women with greater freedom of movement obtained higher levels of antenatal care and were more likely to use safe delivery care. The influence of women's autonomy on the use of health care appears to be as important as other known determinants such as education.     

Explaining Asia's “Missing Women”: A New Look at the Data

The fact that millions of females are “missing” in East Asia and South Asia has been attributed to cultural factors that support strong son preference in these countries. A widely disseminated paper by Emily Oster argues that a large part of this phenomenon can be attributed to excessively masculine sex ratios at birth resulting from maternal infection with hepatitis B. If her thesis is true, current policies to address this problem would need to be reframed to include biological factors in addition to cultural factors. The data show, however, that whether or not females “go missing” is determined by the existing sex composition of the family into which they are conceived. Girls with no older sisters have similar chances of survival as boys. However, girls conceived in families that already have a daughter experience steeply higher probabilities of being aborted or of dying in early childhood. This indicates that cultural factors still provide the overwhelming explanation for the “missing” females.     

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.     

A Modification of a Percentile Estimation Procedure Based on Generalized Polya Urns

Adaptive designs find an important application in the estimation of unknown percentiles for an underlying dose-response curve. A nonparametric adaptive design was suggested by Mugno et al. (2004 Mugno, R.A., Zhus, W., Rosenberger, W.F. (2004). Adaptive urn designs for estimating several percentiles of a dose-response curve. Statist. Med. 23(13):2137–2150.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) to simultaneously estimate multiple percentiles of an unknown dose-response curve via generalized Polya urns. In this article, we examine the properties of the design proposed by Mugno et al. (2004 Mugno, R.A., Zhus, W., Rosenberger, W.F. (2004). Adaptive urn designs for estimating several percentiles of a dose-response curve. Statist. Med. 23(13):2137–2150.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) when delays in observing responses are encountered. Using simulations, we evaluate a modification of the design under varying group sizes. Our results demonstrate unbiased estimation with minimal loss in efficiency when compared to the original compound urn design.     

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