Sex in an Imperial war zone: transnational encounters in Second World War India

This article suggests how the waging of war in an imperial setting may have reshaped military and civilian relations in India from 1939-45. The number of troops stationed in India had repercussions for society and local politics. The article investigates widespread prostitution as one aspect of the gendered wartime economy. Indian prostitution was closely linked to militarization and to the effects of the 1943 Bengal famine. The article also argues this was symptomatic of a more far-reaching renegotiation of the interactions between men and women in the Indian Empire of the 1940s. Other Indian, European, North American and Anglo-Indian women worked as nurses, with the Red Cross and in a variety of roles towards the war effort. Women were subject to new social and sexual demands due to the increased numbers of troops stationed in India in the 1940s.     

Reviewing building construction statistics in Turkey: Stakeholders’ perspective

Construction is one of the industries of strategic economic importance. Governments using their national institutions are involved in the collection of economic statistics, which indicate the state of their economy in the form of an annual time series. However, the completeness and accuracy of these statistics is frequently questioned by the stakeholders within the sector. Therefore there is an increasing need for accurate, manageable and reliable statistics to enable sound analysis of the construction sector. The main purpose of this research is to review building construction statistics in Turkey. The objectives of the review are to 1) engage with users to ascertain their views on the statistics and to identify their needs, and 2) investigate whether the statistics in their present form continue to meet user needs, and identify options for change. As part of the review, a user consultation exercise is carried out to identify whether user needs are met by the statistics, as well as to ascertain users’ views on the European Statistical System (ESS) key dimensions of the quality of the statistics. This review of building construction statistics will facilitate statistical information to be presented in a more comprehensive manner, with sufficient detail, highly reliable in terms of quality and responsive to dynamic changes of the sector (such as inflation and structural adjustment policies).     

Construction of Balanced Incomplete Block Designs Using Cyclic Shifts

Balanced incomplete block designs (BIBDs) play important role in design of experiments, especially in field experiments. These designs ensure that treatments are compared with equal precision. Several methods are available in the literature to construct BIBDs but in this article some infinite series of these designs are presented by method of cyclic shifts. This method expresses some standard properties of a design just through examining the sets of shifts rather than studying the whole design.     

Best linear unbiased and invariant estimation in location-scale families based on double-ranked set sampling

Abstract

In this article, we propose the best linear unbiased estimators (BLUEs) and best linear invariant estimators (BLIEs) for the unknown parameters of location-scale family of distributions based on double-ranked set sampling (DRSS) using perfect and imperfect rankings. These estimators are then compared with the BLUEs and BLIEs based on ranked set sampling (RSS). It is shown that under perfect ranking, the proposed estimators are uniformly better than the BLUEs and BLIEs obtained via RSS. We also propose the best linear unbiased quantile (BLUQ) and the best linear invariant quantile (BLIQ) estimators for normal distribution under DRSS. It is observed that the proposed quantile estimators are more efficient than the BLUQ and BLIQ estimators based on RSS for both perfect and imperfect orderings.     

On Properties of Predictive Priors in Linear Models

Utilizing the notion of matching predictives as in Berger and Pericchi, we show that for the conjugate family of prior distributions in the normal linear model, the symmetric Kullback-Leibler divergence between two particular predictive densities is minimized when the prior hyperparameters are taken to be those corresponding to the predictive priors proposed in Ibrahim and Laud and Laud and Ibrahim. The main application for this result is for Bayesian variable selection.     

Structured measurement error in nutritional epidemiology: applications in the Pregnancy, Infection, and Nutrition (PIN) Study

Preterm birth, defined as delivery before 37 completed weeks' gestation, is a leading cause of infant morbidity and mortality. Identifying factors related to preterm delivery is an important goal of public health professionals who wish to identify etiologic pathways to target for prevention. Validation studies are often conducted in nutritional epidemiology in order to study measurement error in instruments that are generally less invasive or less expensive than "gold standard" instruments. Data from such studies are then used in adjusting estimates based on the full study sample. However, measurement error in nutritional epidemiology has recently been shown to be complicated by correlated error structures in the study-wide and validation instruments. Investigators of a study of preterm birth and dietary intake designed a validation study to assess measurement error in a food frequency questionnaire (FFQ) administered during pregnancy and with the secondary goal of assessing whether a single administration of the FFQ could be used to describe intake over the relatively short pregnancy period, in which energy intake typically increases. Here, we describe a likelihood-based method via Markov Chain Monte Carlo to estimate the regression coefficients in a generalized linear model relating preterm birth to covariates, where one of the covariates is measured with error and the multivariate measurement error model has correlated errors among contemporaneous instruments (i.e. FFQs, 24-hour recalls, and/or biomarkers). Because of constraints on the covariance parameters in our likelihood, identifiability for all the variance and covariance parameters is not guaranteed and, therefore, we derive the necessary and suficient conditions to identify the variance and covariance parameters under our measurement error model and assumptions. We investigate the sensitivity of our likelihood-based model to distributional assumptions placed on the true folate intake by employing semi-parametric Bayesian methods through the mixture of Dirichlet process priors framework. We exemplify our methods in a recent prospective cohort study of risk factors for preterm birth. We use long-term folate as our error-prone predictor of interest, the food-frequency questionnaire (FFQ) and 24-hour recall as two biased instruments, and serum folate biomarker as the unbiased instrument. We found that folate intake, as measured by the FFQ, led to a conservative estimate of the estimated odds ratio of preterm birth (0.76) when compared to the odds ratio estimate from our likelihood-based approach, which adjusts for the measurement error (0.63). We found that our parametric model led to similar conclusions to the semi-parametric Bayesian model.     

Missing covariates in generalized linear models when the missing data mechanism is non-ignorable

We propose a method for estimating parameters in generalized linear models with missing covariates and a non-ignorable missing data mechanism. We use a multinomial model for the missing data indicators and propose a joint distribution for them which can be written as a sequence of one-dimensional conditional distributions, with each one-dimensional conditional distribution consisting of a logistic regression. We allow the covariates to be either categorical or continuous. The joint covariate distribution is also modelled via a sequence of one-dimensional conditional distributions, and the response variable is assumed to be completely observed. We derive the E- and M-steps of the EM algorithm with non-ignorable missing covariate data. For categorical covariates, we derive a closed form expression for the E- and M-steps of the EM algorithm for obtaining the maximum likelihood estimates (MLEs). For continuous covariates, we use a Monte Carlo version of the EM algorithm to obtain the MLEs via the Gibbs sampler. Computational techniques for Gibbs sampling are proposed and implemented. The parametric form of the assumed missing data mechanism itself is not `testable' from the data, and thus the non-ignorable modelling considered here can be viewed as a sensitivity analysis concerning a more complicated model. Therefore, although a model may have `passed' the tests for a certain missing data mechanism, this does not mean that we have captured, even approximately, the correct missing data mechanism. Hence, model checking for the missing data mechanism and sensitivity analyses play an important role in this problem and are discussed in detail. Several simulations are given to demonstrate the methodology. In addition, a real data set from a melanoma cancer clinical trial is presented to illustrate the methods proposed.     

The Progress of Financial Market Integration in East Asia

The study investigates the progress of financial market integration in selected East Asian countries after the 1997 financial crisis. Adopting Johansen (Econometrica 59:1551–1580, 1991) multivariate cointegration on the region’s credit and stock markets, the study finds only partial cointegration in both markets which imply a low level of integration. However, for regional stock markets, the result suggests that the level of integration has been improving after the crisis.     

Tagungsbericht der Veranstalter

Tagungsbericht

Tagungsbericht der Veranstalter