Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Real and Complex Analysis

During the past decade, possible advancement in timing of puberty has been reported in the United States. In addition, early pubertal development and an increased incidence of sexual precocity have been noticed in children, primarily girls, migrating for foreign adoption in several Western European countries. These observations are raising the issues of current differences and secular trends in timing of puberty in relation to ethnic, geographical, and socioeconomic background. None of these factors provide an unequivocal explanation for the earlier onset of puberty seen in the United States. In the formerly deprived migrating children, refeeding and catch-up growth may prime maturation. However, precocious puberty is seen also in some nondeprived migrating children. Attention has been paid to the changing milieu after migration, and recently, the possible role of endocrine- disrupting chemicals from the environment has been considered. These observations urge further study of the onset of puberty as a possible sensitive and early marker of the interactions between environmental conditions and genetic susceptibility that can influence physiological and pathological processes.

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The timing of normal puberty and the age limits of sexual precocity: variations around the world, secular trends, and changes after migration

Variance-based structural equation modeling is extensively used in information systems research, and many related findings may have been distorted by hidden collinearity. This is a problem that may extent to multivariate analyses in general, in the field of information systems as well as in many other fields. In multivariate analyses, collinearity is usually assessed as a predictor-predictor relationship phenomenon, where two or more predictors are checked for redundancy. This type of assessment addresses vertical, or “classic,” collinearity. However, another type of collinearity may also exist, called here “lateral” collinearity. It refers to predictor-criterion collinearity. Lateral collinearity problems are exemplified based on an illustrative variance-based structural equation modeling analysis. The analysis employs WarpPLS 2.0, with the results double-checked with other statistical analysis software tools. It is shown that standard validity and reliability tests do not properly capture lateral collinearity. A new approach for the assessment of both vertical and lateral collinearity in variance-based structural equation modeling is proposed and demonstrated in the context of the illustrative analysis.

Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations

Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques

The problem of tracking multiple targets in the presence of clutter is addressed. The joint probabilistic data association (JPDA) algorithm has been previously reported to be suitable for this problem in that it makes few assumptions and can handle many targets as long as the clutter density is not very high. However, the complexity of this algorithm increases rapidly with the number of targets and returns. An approximation of the JPDA that uses an analog computational network to solve the data association problem is suggested. The problem is viewed as that of optimizing a suitably chosen energy function. Simple neural-network structures for the approximate minimization of such functions have been proposed by other researchers. The analog network used offers a significant degree of parallelism and thus can compute the association probabilities more rapidly. Computer simulations indicate the ability of the algorithm to track many targets simultaneously in the presence of moderately dense clutter. >

Neural solution to the multitarget tracking data association problem

Nonparametric classes of life distributions are usually based on the pattern of aging in some sense. The common parametric families of life distributions also feature monotone aging. In this paper we consider the class of log-concave distributions and the subclass of concave distributions. The work is motivated by the fact that most of the common parametric models of life distributions (including Weibull, Gamma, log-normal, Pareto, and Gompertz distributions) are log-concave, while the remaining life of maintained and old units tend to have a concave distribution. The classes of concave and log-concave distributions do not feature monotone aging. Nevertheless, these two classes are shown to have several interesting and useful properties. We examine the closure of these classes under a number of reliability operations, and provide sharp reliability bounds for nonmaintained and maintained units having life distribution belonging to these classes. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 419–433, 1999

Log-concave and concave distributions in reliability

The problem of estimating the parameters of a non-Gaussian autoregressive process is addressed. Departure of the driving noise from Gaussianity is shown to have the potential for improving their accuracy of the estimation of the parameters. While the standard linear prediction techniques are computationally efficient, they show a substantial loss of efficiency when applied to non-Gaussian processes. A maximum-likelihood estimator is proposed for more precise estimation of the parameters of these processes coupled with a realistic non-Gaussian model for the driving noise. The performance is compared to that of the linear prediction estimator and, as expected, the maximum-likelihood estimator displays a marked improvement. >

Debasis Sengupta

Papers

Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques

Neural solution to the multitarget tracking data association problem

Log-concave and concave distributions in reliability

Efficient estimation of parameters for non-Gaussian autoregressive processes

The National Center for Health Statistics reference and the growth of Indian adolescent boys