Estimation and tests for a longitudinal regression model based on the Markov chain

In this paper, the dependence of transition probabilities on covariates and a test procedure for covariate dependent Markov models are examined. The nonparametric test for the role of waiting time proposed by Jones and Crowley [M. Jones, J. Crowley, Nonparametric tests of the Markov model for survival data Biometrika 79 (3) (1992) 513–522] has been extended here to transitions and reverse transitions. The limitation of the Jones and Crowley method is that it does not take account of other covariates that might have association with the probabilities of transition. A simple test procedure is proposed that can be employed for testing: (i) the significance of association between covariates and transition probabilities, and (ii) the impact of waiting time on the transition probabilities. The procedure is illustrated using panel data on hospitalization of the elderly population in the USA from the Health and Retirement Survey (HRS).     

The Case of the Disappearing Mexican Americans: An Ethnic-Identity Mystery

We examine the issue of identification stability for U.S.-born Mexican Americans, by far the largest of the ethnic groups growing as a result of contemporary immigration. We demonstrate a significant exodus from the group as identified by the census. Although changes in the wording of the census question may have contributed to this loss, a major portion, as revealed by comparisons of birth cohorts across the 1980, 1990, and 2000 Censuses, occurs because individuals who identified themselves as Mexican American at an earlier point in time do not do so at a later point. In addition, there are exits that occur between generations because of past intermarriage, evident in the number of non-Hispanics who claim Mexican ancestry. The losses appear to be accounted for by two kinds of identity shifts: toward identities that have a mainstream character and thus appear reflect conventional assimilation; and toward identities that have a pan-ethnic character, i.e., with Hispanics or Latinos. These exits are selective, but in complex and partially off-setting ways. Nevertheless, the comparison of the characteristics of U.S.-born members of the Mexican–American group over time is likely to be affected by changing patterns of identification.

Labour Market and Human Capital Behaviour of Immigrant Couples: Evidence from Australia

We examine the labour market and human capital investment behaviour of immigrant couples in Australia. The family investment hypothesis (FIH) states that immigrant husbands, whom the FIH assumes to be the primary earners in the family, will invest more in their human capital than immigrant wives, whom the FIH assumes to be secondary earners. Using longitudinal data from Household, Income and Labour Dynamics in Australia (HILDA) for the period 2001 to 2014, we do not find support for the FIH. Instead, we find that foreign-born husbands and wives share similar labour market assimilation patterns with respect to hours of work and wages. Our estimates suggest that immigrant wives' labour market behaviour can be better explained by their own long-term career progression and labour market assimilation, than through supporting their husbands' labour market assimilation.     

Salinization of the Bangladesh Delta worsens economic precarity

Population and Environment - Anthropogenic environmental changes are having complex effects on all aspects of the hydrological cycle. In estuarine areas, these factors are coalescing...     

Minimum-segment convex drawings of 3-connected cubic plane graphs

A convex drawing of a plane graph G is a plane drawing of G, where each vertex is drawn as a point, each edge is drawn as a straight line segment and each face is drawn as a convex polygon. A maximal segment is a drawing of a maximal set of edges that form a straight line segment. A minimum-segment convex drawing of G is a convex drawing of G where the number of maximal segments is the minimum among all possible convex drawings of G. In this paper, we present a linear-time algorithm to obtain a minimum-segment convex drawing Γ of a 3-connected cubic plane graph G of n vertices, where the drawing is not a grid drawing. We also give a linear-time algorithm to obtain a convex grid drawing of G on an $(\frac{n}{2}+1)\times(\frac {n}{2}+1)$ grid with at most s _n +1 maximal segments, where $s_{n}=\frac{n}{2}+3$ is the lower bound on the number of maximal segments in a convex drawing of G.     

Negotiating identities: the lives of Pakistani and Bangladeshi young disabled people

Research has generally amalgamated minority ethnic (all called 'Asian' or 'black') disabled young people's experiences and failed to acknowledge the multiple aspects of Asian and black disabled identities, for example how the combined attributes of race, ethnicity, religion, gender, culture, class and disability shape their perspectives and experiences. In an attempt to address this issue my doctoral research explored the experiences and perspectives of 13 young Pakistani and Bangladeshi disabled people. By drawing on the substantive and theoretical findings which emerged from my analysis in this paper I shall consider how multiple aspects of identity, such as ethnicity, disability and gender, affect this population's identity and self-image and how this makes their experiences different from white disabled young people and other minority groups' experiences.     

Inference in multivariate linear regression models with elliptically distributed errors

In this study we investigate the problem of estimation and testing of hypotheses in multivariate linear regression models when the errors involved are assumed to be non-normally distributed. We consider the class of heavy-tailed distributions for this purpose. Although our method is applicable for any distribution in this class, we take the multivariate t-distribution for illustration. This distribution has applications in many fields of applied research such as Economics, Business, and Finance. For estimation purpose, we use the modified maximum likelihood method in order to get the so-called modified maximum likelihood estimates that are obtained in a closed form. We show that these estimates are substantially more efficient than least-square estimates. They are also found to be robust to reasonable deviations from the assumed distribution and also many data anomalies such as the presence of outliers in the sample, etc. We further provide test statistics for testing the relevant hypothesis regarding the regression coefficients.     

Stringency-based ranking of normality tests

Many parametric statistical inferential procedures in finite samples depend crucially on the underlying normal distribution assumption. Dozens of normality tests are available in the literature to test the hypothesis of normality. Availability of such a large number of normality tests has generated a large number of simulation studies to find a best test but no one arrived at a definite answer as all depends critically on the alternative distributions which cannot be specified. A new framework, based on stringency concept, is devised to evaluate the performance of the existing normality tests. Mixture of t-distributions is used to generate the alternative space. The LR-tests, based on Neyman–Pearson Lemma, have been computed to construct a power envelope for calculating the stringencies of the selected normality tests. While evaluating the stringencies, Anderson–Darling (AD) statistic turns out to be the best normality test.