Catching‐up or Leapfrogging in the Indian IT Service Sector: Windows of Opportunity,Path‐creating,and Moving up the Value Chain

Indian IT service firms are successfully competing with world leaders and research on the industry is emerging. Providing analysis at firm rather than industry level, this article identifies the windows of opportunity open to Indian firms, with the following findings: (i) the catching‐up process by Indian IT firms can be classified as a three‐stage ‘body shopping–offshoring–global delivery model’, comparable with the three steps in original equipment, own‐design and own‐brand manufacturing; (ii) the window of opportunity for Indian firms was primarily the techno‐economic paradigm shift, and secondarily the government's regulation and support of the industry; and (iii) Indian firms initially partially re‐invented their own path by offshoring and created their own global delivery model, gradually moving to higher value‐added services.     

Single Parenthood,Parental Involvement and Students’ Educational Outcomes in Hong Kong

Analyzing data from the Programme for International Student Assessment Hong Kong 2009 and 2012 (n = 7,669), we examined the differences in socioeconomic characteristics of fathers and mothers and levels of parental involvement between two-parent, single-mother, and single-father families in Hong Kong. We found that parents from single-mother and single-father families fare differently in terms of sociodemographic background and parental involvement at home. We also investigated the differences in students’ academic performance among these families. Past studies failed to find any significant effect of single parenthood on students’ academic performance in Hong Kong. We found negative effects of single fatherhood, but not single motherhood, on educational outcomes. The disadvantages of single fatherhood are partially explained by the poorer sociodemographic background and lower levels of parental involvement.     

Oxidative stress levels are reduced in postmenopausal women with exercise training regardless of hormone replacement therapy status

This study investigated whether postmenopausal women on HRT would experience a greater reduction in oxidative stress after 24 weeks of aerobic exercise training compared to postmenopausal women not on HRT. Plasma thiobarbituric acid reactive substances (TBARS), an indicator of oxidative stress, was measured in 48 previously sedentary postmenopausal women on HRT (n = 21) and not on HRT (n = 27) before and after 24 weeks of aerobic exercise training. Baseline levels of TBARS differed significantly between groups after controlling for age, BMI, and fasting blood glucose (P = 0.03). There was a significant reduction in TBARS after 24 weeks of training in the overall group. When analyzed separately, both postmenopausal women on HRT and those not on HRT had a significant reduction in TBARS; however, there was no significant difference between groups (-0.71 +/- 0.14 nmol/ml in non-HRT users vs. -0.50 +/- 0.16 nmol/ml in HRT users; P = 0.33) even after controlling for age, BMI, and baseline levels of TBARS. Our results showed that aerobic exercise training significantly decreased oxidative stress in postmenopausal women; however, both HRT users and non-HRT users benefited equally.     

General cumulative Kullback–Leibler information

The cumulative residual Kullback–Leibler information is defined on the semi-infinite (non negative) interval. In this paper, we extend the cumulative residual Kullback–Leibler information to the whole real line and propose a general cumulative Kullback–Leibler information. We study its application to a test for normality in comparison with some competing test statistics based on the empirical distribution function including the well-known tests applied in practice like Kolmogorov–Smirnov, Cramer–von Mises, Anderson–Darling, and other existing tests.     

Fisher information in progressive hybrid censoring schemes

The hybrid censoring scheme, which is a mixture of Type-I and Type-II censoring schemes, has been extended to the case of progressive censoring schemes by Kundu and Joarder [Analysis of Type-II progressively hybrid censored data, Comput. Stat. Data Anal. 50 (2006), pp. 2509–2528] and Childs et al. [Exact likelihood inference for an exponential parameter under progressive hybrid censoring schemes, in Statistical Models and Methods for Biomedical and Technical Systems, F. Vonta, M. Nikulin, N. Limnios, and C. Huber-Carol, eds., Birkhäuser, Boston, MA, 2007, pp. 323–334]. In this paper, we derive a simple expression for the Fisher information contained in Type-I and Type-II progressively hybrid censored data. An illustrative example is provided applicable to a scaled-exponential distribution to demonstrate our methodologies.     

Segmental modeling of changing immunologic response for CD4 data with skewness,missingness and dropout

In clinical practice, the profile of each subject's CD4 response from a longitudinal study may follow a ‘broken stick’ like trajectory, indicating multiple phases of increase and/or decline in response. Such multiple phases (changepoints) may be important indicators to help quantify treatment effect and improve management of patient care. Although it is a common practice to analyze complex AIDS longitudinal data using nonlinear mixed-effects (NLME) or nonparametric mixed-effects (NPME) models in the literature, NLME or NPME models become a challenge to estimate changepoint due to complicated structures of model formulations. In this paper, we propose a changepoint mixed-effects model with random subject-specific parameters, including the changepoint for the analysis of longitudinal CD4 cell counts for HIV infected subjects following highly active antiretroviral treatment. The longitudinal CD4 data in this study may exhibit departures from symmetry, may encounter missing observations due to various reasons, which are likely to be non-ignorable in the sense that missingness may be related to the missing values, and may be censored at the time of the subject going off study-treatment, which is a potentially informative dropout mechanism. Inferential procedures can be complicated dramatically when longitudinal CD4 data with asymmetry (skewness), incompleteness and informative dropout are observed in conjunction with an unknown changepoint. Our objective is to address the simultaneous impact of skewness, missingness and informative censoring by jointly modeling the CD4 response and dropout time processes under a Bayesian framework. The method is illustrated using a real AIDS data set to compare potential models with various scenarios, and some interested results are presented.     

Estimation of the Change Point in Monitoring the Process Mean and Variance

Knowing the time of a process change could lead to quicker identification of the special cause and less process down time, as well as help to reduce the probability of incorrectly identifying the special cause. In this article, we propose the maximum likelihood estimator (MLE) for the process change point when a control chart with the fixed sampling rate (FSR) scheme or the variable sampling rate (VSR) scheme is used in monitoring a process to detect changes in the process mean and/or variance of a normal quality variable. We investigate the performance of this estimator when it is used in various types of control charts.     

A Nonparametric Test Procedure Based on a Group of Quantile Tests

In this article, we consider nonparametric test procedures based on a group of quantile test statistics. We consider the quadratic form for the two-sided test and the maximal and summing types of statistics for the one-sided alternatives. Then we derive the null limiting distributions of the proposed test statistics using the large sample approximation theory. Also, we consider applying the permutation principle to obtain the null distribution. In this vein, we may consider the supremum type, which should use the permutation principle for obtaining the null distribution. Then we illustrate our procedure with an example and compare the proposed tests with other existing tests including the individual quantile tests by obtaining empirical powers through simulation study. Also, we comment on the related discussions to this testing procedure as concluding remarks. Finally we prove the lemmas and theorems in the appendices.     

Robust Regression Using Data Partitioning and M-Estimation

We propose a new robust regression estimator using data partition technique and M estimation (DPM). The data partition technique is designed to define a small fixed number of subsets of the partitioned data set and to produce corresponding ordinary least square (OLS) fits in each subset, contrary to the resampling technique of existing robust estimators such as the least trimmed squares estimator. The proposed estimator shares a common strategy with the median ball algorithm estimator that is obtained from the OLS trial fits only on a fixed number of subsets of the data. We examine performance of the DPM estimator in the eleven challenging data sets and simulation studies. We also compare the DPM with the five commonly used robust estimators using empirical convergence rates relative to the OLS for clean data, robustness through mean squared error and bias, masking and swamping probabilities, the ability of detecting the known outliers, and the regression and affine equivariances.