Computational tools for comparing asymmetric GARCH models via Bayes factors

In this paper we use Markov chain Monte Carlo (MCMC) methods in order to estimate and compare GARCH models from a Bayesian perspective. We allow for possibly heavy tailed and asymmetric distributions in the error term. We use a general method proposed in the literature to introduce skewness into a continuous unimodal and symmetric distribution. For each model we compute an approximation to the marginal likelihood, based on the MCMC output. From these approximations we compute Bayes factors and posterior model probabilities. (C) 2012 IMACS. Published by Elsevier B.V. All rights reserved.

Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.

Bivariate gamma-geometric law and its induced Levy process

In this article we introduce a three-parameter extension of the bivariate exponential-geometric (BEG) law (Kozubowski and Panorska, 2005) [4]. We refer to this new distribution as the bivariate gamma-geometric (BGG) law. A bivariate random vector (X, N) follows the BGG law if N has geometric distribution and X may be represented (in law) as a sum of N independent and identically distributed gamma variables, where these variables are independent of N. Statistical properties such as moment generation and characteristic functions, moments and a variance-covariance matrix are provided. The marginal and conditional laws are also studied. We show that BBG distribution is infinitely divisible, just as the BEG model is. Further, we provide alternative representations for the BGG distribution and show that it enjoys a geometric stability property. Maximum likelihood estimation and inference are discussed and a reparametrization is proposed in order to obtain orthogonality of the parameters. We present an application to a real data set where our model provides a better fit than the BEG model. Our bivariate distribution induces a bivariate Levy process with correlated gamma and negative binomial processes, which extends the bivariate Levy motion proposed by Kozubowski et al. (2008) [6]. The marginals of our Levy motion are a mixture of gamma and negative binomial processes and we named it BMixGNB motion. Basic properties such as stochastic self-similarity and the covariance matrix of the process are presented. The bivariate distribution at fixed time of our BMixGNB process is also studied and some results are derived, including a discussion about maximum likelihood estimation and inference. (C) 2012 Elsevier Inc. All rights reserved.

Assessment of variance components in nonlinear mixed-effects elliptical models

The issue of assessing variance components is essential in deciding on the inclusion of random effects in the context of mixed models. In this work we discuss this problem by supposing nonlinear elliptical models for correlated data by using the score-type test proposed in Silvapulle and Silvapulle (1995). Being asymptotically equivalent to the likelihood ratio test and only requiring the estimation under the null hypothesis, this test provides a fairly easy computable alternative for assessing one-sided hypotheses in the context of the marginal model. Taking into account the possible non-normal distribution, we assume that the joint distribution of the response variable and the random effects lies in the elliptical class, which includes light-tailed and heavy-tailed distributions such as Student-t, power exponential, logistic, generalized Student-t, generalized logistic, contaminated normal, and the normal itself, among others. We compare the sensitivity of the score-type test under normal, Student-t and power exponential models for the kinetics data set discussed in Vonesh and Carter (1992) and fitted using the model presented in Russo et al. (2009). Also, a simulation study is performed to analyze the consequences of the kurtosis misspecification.

IMPROVEMENT TO THE MARGINAL COPING FIT OF COMMERCIALLY PURE TITANIUM CAST IN PHOSPHATE-BONDED INVESTMENT BY USING A SIMPLE PATTERN COATING TECHNIQUE

Statement of problem. Coatings of zirconite, Y2O3 or ZrO2 on wax patterns before investing in phosphate-bonded investments have been recommended to reduce the reaction layer in titanium castings, but they are not easily obtainable. Spinel-based investments are relatively stable with molten titanium and could be used as coatings to improve the quality of castings made with those investments. Purpose. The purpose of this study was to evaluate the effect of pattern coating with a commercial spinel-based investment before investing in 1 of 3 phosphate-bonded investments on the marginal coping fit and surface roughness of commercially pure titanium castings. Material and methods. Ten square acrylic resin patterns (12 x 12 x 2 mm) per group were invested in the phosphate-bonded investments Rematitan Plus (RP), Rema Exakt (RE), and Castorit Super C (CA) with or without a coating of the spinel-based investment, Rematitan Ultra (RU). After casting, the specimens were cleaned and the surface roughness was measured with a profilometer. Copings for dental implants with conical abutment were invested, eliminated, and cast as previously described. The copings were cleaned and misfit was measured with a profile projector (n=10). For both tests, the difference between the mean value of RU only and each value of the phosphate-bonded investment was calculated, and the data were analyzed by 2-way ANOVA and Tukey's HSD test (alpha=.05). In addition, the investment roughness was measured in bar specimens (30 x 10 x 10 mm), and the data (n=10) were analyzed by 1-way ANOVA and Tukey's HSD post hoc test (alpha=.05). Results. Two-way ANOVA for casting surface roughness was significant because of the investment, the coating technique, and the interaction between variables. One-way ANOVA was performed to prove the interaction term, and Tukey's post hoc test showed that RP with coating had the lowest mean, while RP had the highest. CA with coating was not different from RP with coating or CA without coating. RE with coating was similar to CA, while RE was different from all groups. For coping marginal fit, the 2-way ANOVA was significant for the investment, the coating technique, and the interaction between variables. The interaction was analyzed by1-way ANOVA and Tukey's HSD test that showed no significant difference among the coated groups, which had better marginal fit than the groups without coating. Among the groups without coating, CA had significant lower marginal misfit than RP, while RE was not different from CA and RP. For the investment surface roughness, the 1-way ANOVA was significant. CA and RU were smoother than RE and RP (P<.001). Conclusions. The coating technique improved the quality of castings fabricated with phosphate-bonded investments. (J Prosthet Dent 2012;108:51-57)

Do pets reduce the likelihood of sudden unexplained death in epilepsy?

Purpose: To assess the relationship between the presence of pets in homes of epilepsy patients and the occurrence of sudden unexpected death in epilepsy (SUDEP). Methods: Parents or relatives of SUDEP patients collected over a ten-year period (2000-2009) in a large epilepsy unit were asked if the patient lived together with any domestic pet at the time of death or not. Patients who did not experience SUDEP served as controls. Results and conclusions: Eleven out of the 1092 included patients (1%) experienced SUDEP, all with refractory symptomatic epilepsy, but none of them had pets in their homes at the time of death. In contrast, the frequency of pet-ownership in the control group (n = 1081) was 61%. According to previous studies there are some indications that human health is directly related to companionship with animals in a way that domestic animals prevent illness and facilitate recovery of patients. Companion animals can buffer reactivity against acute stress, diminish stress perception and improve physical health. These factors may reduce cardiac arrhythmias and seizure frequency, factors related to SUDEP. Companion animals may have a positive effect on well-being, thus irnproving epilepsy outcome. (c) 2012 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

A Poisson mixed model with nonnormal random effect distribution

In this paper, we propose a random intercept Poisson model in which the random effect is assumed to follow a generalized log-gamma (GLG) distribution. This random effect accommodates (or captures) the overdispersion in the counts and induces within-cluster correlation. We derive the first two moments for the marginal distribution as well as the intraclass correlation. Even though numerical integration methods are, in general, required for deriving the marginal models, we obtain the multivariate negative binomial model from a particular parameter setting of the hierarchical model. An iterative process is derived for obtaining the maximum likelihood estimates for the parameters in the multivariate negative binomial model. Residual analysis is proposed and two applications with real data are given for illustration. (C) 2011 Elsevier B.V. All rights reserved.

Do charges to freight trucks and trains in the European Union reflect their social marginal costs? Application to the case of Spain

In the last few years, the European Union (EU) has become greatly concerned about the environmental costs of road transport in Europe as a result of the constant growth in the market share of trucks and the steady decline in the market share of railroads. In order to reverse this trend, the EU is promoting the implementation of additional charges for heavy goods vehicles (HGV) on the trunk roads of the EU countries. However, the EU policy is being criticised because it does not address the implementation of charges to internalise the external costs produced by automobiles and other transport modes such as railroad. In this paper, we first describe the evolution of the HGV charging policy in the EU, and then assess its practical implementation across different European countries. Second, and of greater significance, by using the case study of Spain, we evaluate to what extent the current fees on trucks and trains reflect their social marginal costs, and consequently lead to an allocative-efficient outcome. We found that for the average case in Spain the truck industry meets more of the marginal social cost produced by it than does the freight railroad industry. The reason for this lies in the large sums of money paid by truck companies in fuel taxes, and the subsidies that continue to be granted by the government to the railroads.

Likelihood-based inference for power distributions

This paper considers likelihood-based inference for the family of power distributions. Widely applicable results are presented which can be used to conduct inference for all three parameters of the general location-scale extension of the family. More specific results are given for the special case of the power normal model. The analysis of a large data set, formed from density measurements for a certain type of pollen, illustrates the application of the family and the results for likelihood-based inference. Throughout, comparisons are made with analogous results for the direct parametrisation of the skew-normal distribution.