Examining the Effects of a Service-Trained Facility Dog on Stress in Children Undergoing Forensic Interview for Allegations of Child Sexual Abuse

Disclosure of child sexual abuse can be a stressful experience for the child. Gaining a better understanding of how best to serve the child, while preserving the quality of their disclosure, is an ever-evolving process. The data to answer this question come from 51 children aged 4–16 (M = 9.1, SD = 3.5), who were referred to a child advocacy center in Virginia for a forensic interview (FI) following allegations of sexual abuse. A repeated measures design was conducted to examine how the presence of a service-trained facility dog (e.g. animal-assisted intervention (AAI) may serve as a mode of lowering stress levels in children during their FIs. Children were randomized to one of the two FI conditions: experimental condition (service-trained facility dog present-AAI) or control condition (service-trained facility dog not present- standard forensic interview). Stress biomarkers salivary cortisol, alpha-amylase, immunoglobulin A (IgA), heart rate, and blood pressure, and Immunoglobulin A were collected before and after the FI. Self-report data were also collected. Results supported a significant decrease in heart rate for those in the experimental condition (p = .0086) vs the control condition (p = .4986). Regression models revealed a significant decrease in systolic and diastolic blood pressure in the experimental condition (p = .03285) and (p = .04381), respectively. Statistically significant changes in alpha-amylase and IgA were also found in relation to disclosure and type of offense. The results of this study support the stress reducing effects of a service-trained facility dog for children undergoing FI for allegations of child sexual abuse.     

Infinite Parameter Estimates in Logistic Regression, with Application to Approximate Conditional Inference

This paper discusses recovery of information regarding logistic regression parameters in cases when maximum likelihood estimates of some parameters are infinite. An algorithm for detecting such cases and characterizing the divergence of the parameter estimates is presented. A method for fitting the remaining parameters is also presented . All of these methods rely only on sufficient statistics rather than less aggregated quantities, as required for inference according to the method of Kolassa & Tanner (1994). These results are applied to approximate conditional inference via saddlepoint methods. Specifically, the double saddlepoint method of Skovgaard (1987) is adapted to the case when the solution to the saddlepoint equations exists as a point at infinity     

Bounding convergence rates for Markov chains: An example of the use of computer algebra

Kolassa and Tanner (J. Am. Stat. Assoc. (1994) 89, 697–702) present the Gibbs-Skovgaard algorithm for approximate conditional inference. Kolassa (Ann Statist. (1999), 27, 129–142) gives conditions under which their Markov chain is known to converge. This paper calculates explicity bounds on convergence rates in terms calculable directly from chain transition operators. These results are useful in cases like those considered by Kolassa (1999).     

Confidence intervals for parameters lying in a random polygon

This paper presents algorithms for computing confidence intervals and regions for elements of a parameter vector when the signs of linear combinations of unknown parameters are observed, but the coefficients contain experimental error. These methods were proposed in the geochemical literature by Kolassa (1992) as a method specific to petrology. Experimental data are used to give linear constraints, involving quantities measured with error, on unknown free energies and entropies of a chemical reaction. Confidence intervals are given for these parameters, and these are compared with more naïve approaches.     

Approximate multivariate conditional inference using the adjusted profile likelihood

The author proposes saddlepoint approximation methods that are adapted to multivariate conditional inference in canonical exponential familles. Several approaches to approximating conditional discrete distributions involve dividing an approximation to the full joint mass function, summed over tail regions of interest, by an approximate marginal density. The author first approximates this conditional likelihood by the adjusted profile likelihood, and then applies a multivariate saddlepoint approximation. He also presents formulas to aid in performing simultaneously the profiling and maximizing steps.     

Saddlepoint Approximation for the Distribution of the Modified Signed Root of Likelihood Ratio Statistics Near the Mean

The Tracy-Singh product, which can be viewed as generalized Kronecher product, is studied and used widely in matrix theory and statistics. Therefore, some well-known Kantorovich inequalities are generalized to Tracy-Singh product in this article. Furthermore, some applications to statistics are presented.     

A Practical Procedure to Find Matching Priors for Frequentist Inference

We present a practical way to find matching priors via the use of saddlepoint approximations and obtain p-values of tests of an interest parameter in the presence of nuisance parameters. The advantages of our procedure are the flexibility in choosing different initial conditions so that one may adjust the performance of a test, and the less intensive computational efforts compared to a Markov Chain Monto Carlo method.     

Algorithms for approximate conditional inference

This paper presents a method for listing the sample space for a conditional distribution in a discrete generalized linear model. This tabulation is used in conjunction with saddlepoint methods to approximate the associated conditional probabilities. These probabilities are used to calculate conditional p-values.     

A Proof of the Asymptotic Equivalence of Two-Tail Probability Approximations

A modified efficient jump algorithm is proposed for the Markov Chain Monte Carlo draws of the exponential power distribution. Bayesian inference based on the exponential power error term and that on the normal error term are compared. Unbiasedness of the LAD estimator is proven.