Balanced 2m factorial designs of resolution v which allow search and estimation of one extra unknown effect, 4 ≤ m ≤ 8

In this paper, we obtain balanced resolution V plans for 2^m factorial experiments (4 ≤ m ≤ 8), which have an additional feature. Instead of assuming that the three factor and higher order effects are all zero, we assume that there is at most one nonnegligible effect among them; however, we do not know which particular effect is nonnegligible. The problem is to search which effect is non-negligible and to estimate it, along with estimating the main effects and two factor interactions etc., as in an ordinary resolution V design. For every value of N (the number of treatments) within a certain practical range, we present a design using which the search and estimation can be carried out. (Of course, as in all statistical problems, the probability of correct search will depend upon the size of “error” or “noise” present in the observations. However, the designs obtained are such that, at least in the noiseless case, this probability equals 1.) It is found that many of these designs are identical with optimal balanced resolution V designs obtained earlier in the work of Srivastava and Chopra.     

Hypercubic designs and applications

In this paper we develop relatively easy methods for constructing hypercubic designs from symmetrical factorial experiments for t=v ^m treatments with v=2, 3. The proposed methods are easy to use and are flexible in terms of choice of possible block sizes.     

Selection of Binary Variables and Classification by Boosting

We adopt boosting for classification and selection of high-dimensional binary variables for which classical methods based on normality and non singular sample dispersion are inapplicable. Boosting seems particularly well suited for binary variables. We present three methods of which two combine boosting with the relatively classical variable selection methods developed in Wilbur et al. (2002 Wilbur , J. D. , Ghosh , J. K. , Nakatsu , C. H. , Brouder , S. M. , Doerge , R. W. ( 2002 ). Variable selection in high-dimensional multivariate binary data with application to the analysis of microbial community DNA fingerprints . Biometrics 58 : 378 – 386 . [Google Scholar]). Our primary interest is variable selection in classification with small misclassification error being used as validation of proposed method for variable selection. Two of the new methods perform uniformly better than Wilbur et al. (2002 Wilbur , J. D. , Ghosh , J. K. , Nakatsu , C. H. , Brouder , S. M. , Doerge , R. W. ( 2002 ). Variable selection in high-dimensional multivariate binary data with application to the analysis of microbial community DNA fingerprints . Biometrics 58 : 378 – 386 . [Google Scholar]) in one set of simulated and three real life examples.     

Interpenetrating subsampling regression estimation with or without jackknifing

In this paper four regression estimators are considered for a finite population total based on interpenetrating subsamples, two of which are with jackknifing and the other two are without jackknifing. Both theoretical and empirical comparisons of the four proposed estimators are done with respect to bias, variance and mean square error.     

Physiologically-Based Pharmacokinetic Modeling of Genistein in Rats, Part I: Model Development

Genistein is a phytoestrogen-a plant-derived compound that binds to and activates the estrogen receptor-occurring at high levels in soy beans and food products, leading to widespread human exposure. The numerous scientific publications available describing genistein's dosimetry, mechanisms of action, and identified or putative health effects in both experimental animals and humans make it ideal for examination as an example of endocrine-active compound (EAC). We developed a physiologically-based pharmacokinetic (PBPK) model to quantify the internal, target-tissue dosimetry of genistein in adult rats. Complexities of the model include enterohepatic circulation, binding of both genistein and its conjugates to plasma proteins, and the multiple compartments used to describe transport through the bile duct and gastrointestinal tract. Other aspects of the model are simple perfusion-limited transport to the tissue groups and first-order rates of metabolism, uptake, and excretion. We describe here the model structure and initial calibration of the model by fitting to a large data set for Wistar rats. The model structure can be readily extrapolated to describe genistein dosimetry in humans or modified to describe the dosimetry of other phytoestrogens and phenolic EACs. The model does a fair job of capturing the pharmacokinetics. Although it does not describe the interindividual variability and we have not identified a single set of parameters that provide a good fit to the data for both oral and intravenous exposures, we believe it provides a good initial attempt at PBPK modeling for genistein, which can serve as a template for other phytoestrogens and in the design of future experiments and research that can be used to fill data gaps and better estimate model parameters.     

Private Provision of Infrastructure in Emerging Markets: Do Institutions Matter?

Governments in developing countries have encouraged private sector investment to meet the growing demand for infrastructure. According to institutional theory, the role of institutions is paramount in private sector development. A longitudinal dataset of 40 developing economies between 1990 and 2000 is used to test empirically how different institutional structures affect private investment in infrastructure, in particular its volume and frequency. The results indicate that property rights and bureaucratic quality play a significant role in promoting private infrastructure investment. Interestingly, they also suggest that countries with higher levels of corruption attract greater private participation in infrastructure.     

A generalized linear mixed model for longitudinal binary data with a marginal logit link function

Longitudinal studies of a binary outcome are common in the health, social, and behavioral sciences. In general, a feature of random effects logistic regression models for longitudinal binary data is that the marginal functional form, when integrated over the distribution of the random effects, is no longer of logistic form. Recently, Wang and Louis (2003) proposed a random intercept model in the clustered binary data setting where the marginal model has a logistic form. An acknowledged limitation of their model is that it allows only a single random effect that varies from cluster to cluster. In this paper, we propose a modification of their model to handle longitudinal data, allowing separate, but correlated, random intercepts at each measurement occasion. The proposed model allows for a flexible correlation structure among the random intercepts, where the correlations can be interpreted in terms of Kendall's τ. For example, the marginal correlations among the repeated binary outcomes can decline with increasing time separation, while the model retains the property of having matching conditional and marginal logit link functions. Finally, the proposed method is used to analyze data from a longitudinal study designed to monitor cardiac abnormalities in children born to HIV-infected women.     

On bahadur's representation of quantiles in nonregular cases

The paper considers Bahadur’s representation of quantiles (see Bahadur (1966), Ann. Math. Statist., 37, 577-580) in cases where the usual assumptions regarding the existence and boundedness of the derivatives of the distribution function in a neighbourhood of the population quantile(s) of interest are not met. Following Kiefer((1967), Ann. Math. Statist., 38, 1323-1342) we provide an exact order of the remainder term in Bahadur’s representation. A weaker result regarding the order of the remainder term is also provided under weaker regularity assumptions.     

On the Non-attainability of Chebyshev Bounds

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