Bayesian inference for a discretely observed stochastic kinetic model

The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observed by means of discrete-time sampling systems, which are often limited in their completeness. In this paper we explore how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model. This simple model describes behaviour typical of many biochemical networks which exhibit auto-regulatory behaviour. Various MCMC algorithms are described and their performance evaluated in several data-poor scenarios. An algorithm based on an approximating process is shown to be particularly efficient.     

Evaluating the power of Minitab's data subsetting lack of fit test in multiple linear regression

Minitab's data subsetting lack of fit test (denoted XLOF) is a combination of Burn and Ryan's test and Utts' test for testing lack of fit in linear regression models. As an alternative to the classical or pure error lack of fit test, it does not require replicates of predictor variables. However, due to the uncertainty about its performance, XLOF still remains unfamiliar to regression users while the well-known classical lack of fit test is not applicable to regression data without replicates. So far this procedure has not been mentioned in any textbooks and has not been included in any other software packages. This study assesses the performance of XLOF in detecting lack of fit in linear regressions without replicates by comparing the power with the classic test. The power of XLOF is simulated using Minitab macros for variables with several forms of curvature. These comparisons lead to pragmatic suggestions on the use of XLOF. The performance of XLOF was shown to be superior to the classical test based on the results. It should be noted that the replicates required for the classical test made itself unavailable for most of the regression data while XLOF can still be as powerful as the classic test even without replicates.     

A spatiotemporal auto-regressive moving average model for solar radiation

Summary.  To investigate the variability in energy output from a network of photovoltaic cells, solar radiation was recorded at 10 sites every 10 min in the Pentland Hills to the south of Edinburgh. We identify spatiotemporal auto-regressive moving average models as the most appropriate to address this problem. Although previously considered computationally prohibitive to work with, we show that by approximating using toroidal space and fitting by matching auto-correlations, calculations can be substantially reduced. We find that a first-order spatiotemporal auto-regressive (STAR(1)) process with a first-order neighbourhood structure and a Matern noise process provide an adequate fit to the data, and we demonstrate its use in simulating realizations of energy output.     

Prediction in the linear model under a linear constraint

In this paper we determine the Gauss–Markov predictor of the nonobservable part of a random vector satisfying the linear model under a linear constraint.     

A characteristic function approach to the biased sampling model,with application to robust logistic regression

Many sampling problems from multiple populations can be considered under the semiparametric framework of the biased, or weighted, sampling model. Included under this framework is logistic regression under case–control sampling. For any model, atypical observations can greatly influence the maximum likelihood estimate of the parameters. Several robust alternatives have been proposed for the special case of logistic regression. However, some current techniques can exhibit poor behavior in many common situations. In this paper a new family of procedures are constructed to estimate the parameters in the semiparametric biased sampling model. The procedures incorporate a minimum distance approach, but are instead based on characteristic functions. The estimators can also be represented as the minimizers of quadratic forms in simple residuals, thus yielding straightforward computation. For the case of logistic regression, the resulting estimators are shown to be competitive with the existing robust approaches in terms of both robustness and efficiency, while maintaining affine equivariance. The approach is developed under the case–control sampling scheme, yet is shown to be applicable under prospective sampling logistic regression as well.     

Influence diagnostics in the capital asset pricing model under elliptical distributions

In this paper we consider the Capital Asset Pricing Model under Elliptical (symmetric) Distributions. This class of distributions, which contains the normal distribution, t, contaminated normal and power exponential, among others, offers a more flexible framework for modelling asset prices or returns. In order to analyze the sensibility to possible outliers and/or atypical returns of the maximum likelihood estimators, the local influence method was implemented. The results are illustrated by using a set of shares from companies who trade in the Chilean Stock Market. Our main conclusion is that symmetric distributions having heavier tails than those of the normal distribution, especially the t distribution with small degrees of freedom, show a better fit and allow the reduction of the influence of atypical returns in the maximum likelihood estimators.     

Navigating the Transition to Multi‐Ethnic Urban High Schools: Changing Ethnic Congruence and Adolescents' School‐Related Affect

This short‐term longitudinal study investigated 918 students' school‐related affect across the transition to high school. The study focused specifically on the moderating effect of change in student ethnic congruence from middle to high school. Results indicate that students experiencing more ethnic incongruence from middle to high school, in particular African American and male students, reported declining feelings of school belonging over time. Moreover, students experiencing ethnic incongruence also had increasing worries about their academic success. These results suggest that the changing school demographics from middle school to high school may negatively impact students' school‐related affect, especially if they move to high schools which include fewer students who are ethnically similar to themselves.     

Life satisfaction in midlife childless and empty-nest men and women

The primary purpose of this study was to contrast the life satisfaction of midlife empty-nest and childless individuals. Seventy-two men and women reported on their satisfaction with 11 areas of life as well as with regard to children. Empty-nest individuals were more satisfied than childless persons with regard to their decisions regarding children; however, the childless individuals were not unhappy with their decisions. Empty-nest individuals were also more positive than childless individuals about contact with children. There were no differences between the two groups with regard to areas of life satisfaction not related to children; nor were there significant gender differences in life satisfaction. The results were discussed in relation to both theory and data concerning midlife satisfaction and adjustment.SMS AssociatesThe research was completed by James E. Bell in partial fulfillment of the requirement for the masters of counseling degree at Arizona State University.     

Children of Adolescent Mothers: Cognitive and Behavioral Status at Age Six

The cognitive and behavioral status at age six years of 185 children of adolescent mothers (60.5% boys) were assessed using the mother (CBCL) and teacher (TRF) report forms of the Child Behavior Checklist and the Bracken Basic Concepts Scale. There was evidence for impairment in all areas of functioning. More than half of the sample exceeded the borderline clinical cutoff (T = 60) on either the CBCL or TRF Total Problem score, and nearly half the sample had Bracken scores that were more than 1 SD below the normative mean. Only 26.9% of the children were in the normal range on all three measures. These results are discussed in terms of implications for social work practice with children and adolescent mother families.