Are old-age security and the utility of children in rural India really unimportant?

Abstract Extract In their recent paper in this journal M. and Carol Vlassoff are to be commended for helping to remedy the dearth of empirical studies on the old-age security motive for children (and particularly sons) in rural areas of developing countries.(1) However, while the questionnaire which they applied to 357 ever-married men in a rural village in Maharashtra state in India is potentially useful, several of the conclusions they derive from it are unwarranted and, if left unquestioned, would undoubtedly have the effect of setting back the serious investigation of the effects of this motive rather than furthering it. The invalid or at least questionable inferences are taken up one at a time in the order of their appearance:     

Temporal prediction of future state occupation in a multistate model from high-dimensional baseline covariates via pseudo-value regression

In many complex diseases such as cancer, a patient undergoes various disease stages before reaching a terminal state (say disease free or death). This fits a multistate model framework where a prognosis may be equivalent to predicting the state occupation at a future time t. With the advent of high-throughput genomic and proteomic assays, a clinician may intent to use such high-dimensional covariates in making better prediction of state occupation. In this article, we offer a practical solution to this problem by combining a useful technique, called pseudo-value (PV) regression, with a latent factor or a penalized regression method such as the partial least squares (PLS) or the least absolute shrinkage and selection operator (LASSO), or their variants. We explore the predictive performances of these combinations in various high-dimensional settings via extensive simulation studies. Overall, this strategy works fairly well provided the models are tuned properly. Overall, the PLS turns out to be slightly better than LASSO in most settings investigated by us, for the purpose of temporal prediction of future state occupation. We illustrate the utility of these PV-based high-dimensional regression methods using a lung cancer data set where we use the patients’ baseline gene expression values.     

Market segmentation: an integrated framework

Contrary to the narrow, demand-oriented approach to market segmentation taken in much of the marketing and planning literature, this article illustrates a broader, integrated framework which includes both the demand and supply sides of the competitive equation. The author argues that starting with ‘product’ characteristics is both an easier and more actionable way of segmenting markets than the traditional marketing approach that typically begins with the customer or ‘people’ characteristics. This approach can be employed by a business to make decisions about entering a market segment, differentiating a brand from its competition, positioning it in the market and communicating its quality to the customers. Customers, too, generally rely on product characteristics to perceive and evaluate the quality claims made by different competitors. Thus, the article bridges an important gap between strategy formulation and strategy implementation: an area often neglected in the strategy literature. The proposed approach focuses not only on customer benefits or needs, but also on the resources necessary to satisfy them and demonstrates this with examples from Apple, Church & Dwight and many other companies. To show a practical application, the article presents a price-quality-benefit segmentation profile of the US toothpaste market which revealed three strategic groups.     

Nonparametric estimation of waiting time distributions in a Markov model based on current status data

We construct nonparametric estimators of state waiting time distribution functions in a Markov multistate model using current status data. This is a particularly difficult problem since neither the entry nor the exit times of a given state are directly observed. These estimators are obtained, using the Markov property, from estimators of counting processes of state entry and exit times, as well as, the size of “at risk” sets of state entry and transitions out of that state. Consistency of our estimators is established. Finite-sample behavior of our estimators is studied by simulation, in which we show that our estimators based on current status data compare well with those based on complete data. We also illustrate our method using a pubertal development data set obtained from the NHANES III [1997. NHANES III Reference Manuals and Reports (CD-ROM). Analytic and Reporting Guidelines: The Third National Health and Nutrition Examination Survey (1988–94). National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD] study.     

Creating value through offshore outsourcing: An integrative framework

This article proposes an analytical framework to explain value creation through offshore outsourcing by addressing a key question: How do firms create value by outsourcing their business functions to foreign external providers? The growing prevalence of offshore outsourcing as a dominant business practice in global business makes this question worthy of further research attention. Situated within the organizational design literature, our proposed value creation framework also draws from strategic resource management, disintegration, location-specific resourcing, and externalization (D–L–E) and contingency perspectives. Our analysis shows that firms embarking on offshore outsourcing create value by effectively managing their internal and external resources in accordance with a changing global environment. The framework has significant implications for theory and practice and suggests avenues for further research.     

Feature selection and machine learning with mass spectrometry data for distinguishing cancer and non-cancer samples

This is a comparative study of various clustering and classification algorithms as applied to differentiate cancer and non-cancer protein samples using mass spectrometry data. Our study demonstrates the usefulness of a feature selection step prior to applying a machine learning tool. A natural and common choice of a feature selection tool is the collection of marginal p-values obtained from t-tests for testing the intensity differences at each m/z ratio in the cancer versus non-cancer samples. We study the effect of selecting a cutoff in terms of the overall Type 1 error rate control on the performance of the clustering and classification algorithms using the significant features. For the classification problem, we also considered m/z selection using the importance measures computed by the Random Forest algorithm of Breiman. Using a data set of proteomic analysis of serum from ovarian cancer patients and serum from cancer-free individuals in the Food and Drug Administration and National Cancer Institute Clinical Proteomics Database, we undertake a comparative study of the net effect of the machine learning algorithm–feature selection tool–cutoff criteria combination on the performance as measured by an appropriate error rate measure.     

Bayesian factor models in characterizing molecular adaptation

Assessing the selective influence of amino acid properties is important in understanding evolution at the molecular level. A collection of methods and models has been developed in recent years to determine if amino acid sites in a given DNA sequence alignment display substitutions that are altering or conserving a prespecified set of amino acid properties. Residues showing an elevated number of substitutions that favorably alter a physicochemical property are considered targets of positive natural selection. Such approaches usually perform independent analyses for each amino acid property under consideration, without taking into account the fact that some of the properties may be highly correlated. We propose a Bayesian hierarchical regression model with latent factor structure that allows us to determine which sites display substitutions that conserve or radically change a set of amino acid properties, while accounting for the correlation structure that may be present across such properties. We illustrate our approach by analyzing simulated data sets and an alignment of lysin sperm DNA.     

Effects of Firm-specific and Country-specific Advantages on Relative Acquisition Size in Service Sector Cross-Border Acquisitions: An Empirical Examination

Using a sample of 348 service sector cross-border acquisitions by U.S. firms in 44 countries during 1990–2006, our study seeks to identify factors that influence relative acquisition size (acquisition transaction value as a percentage of acquiring firm's asset value). Our findings indicate that firm-specific advantages (FSAs) in the form of available financial slack and target industry knowledge were positively associated with relative acquisition size. However, contrary to expectations, we observed a negative relationship between cross-border acquisition experience and relative acquisition size. In addition, our results suggest that country-specific advantages (CSAs) associated with higher market potential, lower political risk, and greater cultural similarity contributed to increased relative acquisition size in service industry cross-border acquisitions. Finally, our analysis reveals that the relationship between available financial slack and relative acquisition size is contingent on cultural similarity with the relationship being more pronounced when cultural similarity is high.