This dissertation identifies key determinants of outbound cross-border mergers and acquisitions (M&As) by emerging Asian acquirers during 2001-2012. Using a zero-inflated model that takes into account different mechanisms governing country pairs that never engage in cross-border M&As and country pairs that actively participate in cross-border M&As, I uncover unique patterns for emerging Asian acquirers. Emerging Asian acquirers originate from countries with lower corporate tax rates than those countries where their targets are located. Furthermore, the negative impact of an international double tax burden is significantly larger than that found in previous studies. While country governance differences and geographical and cultural differences are important determinants of international M&As, relative valuation effects are muted. Coefficients of these determinants vary substantially, depending on whether targets are located in developing or advanced nations. Also, determinants differ considerably between active and non-active players in cross-border M&As. Moreover, comparisons of empirical models illustrate that estimating a non-linear model and taking into account both the bounded nature and non-normal distributions of fractional response variables lead to different inferences from those drawn from a linear model estimated by the ordinary least squares method. Overall, emerging Asian acquirers approach the deals differently from patterns documented in developed markets. So, when evaluating foreign business combinations or devising policies, managers or policymakers should consider these differences.

Dans la plupart des questions écologiques, les phénomènes aléatoires d'intérêt sont spatialement structurés et issus de l'effet combiné de multiples variables aléatoires, observées ou non, et inter-agissant à diverses échelles. En pratique, dès lors que les données de terrain ne peuvent être directement traitées avec des structures spatiales standards, les observations sont généralement considérées indépendantes. Par ailleurs, les modèles utilisés sont souvent basés sur des hypothèses simplificatrices trop fortes par rapport à la complexité des phénomènes étudiés. Dans ce travail, la démarche de modélisation hiérarchique est combinée à certains outils de la statistique spatiale afin de construire des structures aléatoires fonctionnelles "sur-mesure" permettant de représenter des phénomènes spatiaux complexes en écologie des populations. L'inférence de ces différents modèles est menée dans le cadre bayésien avec des algorithmes MCMC. Dans un premier temps, un modèle hiérarchique spatial (Geneclust) est développé pour identifier des populations génétiquement homogènes quand la diversité génétique varie continûment dans l'espace. Un champ de Markov caché, qui modélise la structure spatiale de la diversité génétique, est couplé à un modèle bivarié d'occurrence de génotypes permettant de tenir compte de l'existence d'unions consanguines chez certaines populations naturelles. Dans un deuxième temps, un processus de Poisson composé particulier,appelé loi des fuites, est présenté sous l'angle de vue hiérarchique pour décrire le processus d'échantillonnage d'organismes vivants. Il permet de traiter le délicat problème de données continues présentant une forte proportion de zéros et issues d'échantillonnages à efforts variables. Ce modèle est également couplé à différents modèles sur grille (spatiaux, régionalisés) afin d'introduire des dépendances spatiales entre unités géographiques voisines puis, à un champ géostatistique bivarié construit par convolution sur grille discrète afin de modéliser la répartition spatiale conjointe de deux espèces. Les capacités d'ajustement et de prédiction des différents modèles hiérarchiques proposés sont comparées aux modèles traditionnellement utilisés à partir de simulations et de jeux de données réelles (ours bruns de Suède, invertébrés épibenthiques du Golfe-du-Saint-Laurent (Canada)).

The use of Light Detection and Ranging (LiDAR) information is gaining popularity, however its use has been limited in deciduous forests. This thesis describes two studies using LiDAR data in an Eastern Kentucky deciduous forest. The first study quantifies vertical error of LiDAR derived digital elevation models (DEMs) which describe the forests terrain. The study uses a new method which eliminates Global Positioning System (GPS) error. The study found that slope and slope variability both significantly affect DEM error and should be taken in to account when using LiDAR derived DEMs. The second study uses LiDAR derived forest vegetation and terrain metrics to predict terrestrial Plethodontid salamander abundance across the forest. This study used night time visual encounter surveys coupled with zero-inflation modeling to predict salamander abundance based on environmental covariates. We focused on two salamander species, Plethodon glutinosus and Plethodon kentucki. Our methods produced two different best fit models for the two species. Plethodon glutinosus included vegetation height standard deviation and water flow accumulation covariates, while Plethodon kentucki included only canopy cover as a covariate. These methods are applicable to many different species and can be very useful for focusing management efforts and understanding species distributions across the landscape.

Generalized linear mixture models (GLMM) are widely used in statistical applications to model count and binary data. We consider the problem of nonparametric likelihood estimation of mixing distributions in GLMM's with multiple random effects. The log-likelihood to be maximized has the general form l(G)=Σi log∫f(yi,γ) dG(γ) where f(.,γ) is a parametric family of component densities, yi is the ith observed response dependent variable, and G is a mixing distribution function of the random effects vector γ defined on Ω. The literature presents many algorithms for maximum likelihood estimation (MLE) of G in the univariate random effect case such as the EM algorithm (Laird, 1978), the intra-simplex direction method, ISDM (Lesperance and Kalbfleish, 1992), and vertex exchange method, VEM (Bohning, 1985). In this dissertation, the constrained Newton method (CNM) in Wang (2007), which fits GLMM's with random intercepts only, is extended to fit clustered datasets with multiple random effects. Owing to the general equivalence theorem from the geometry of mixture likelihoods (see Lindsay, 1995), many NPMLE algorithms including CNM and ISDM maximize the directional derivative of the log-likelihood to add potential support points to the mixing distribution G. Our method, Direct Search Directional Derivative (DSDD), uses a directional search method to find local maxima of the multi-dimensional directional derivative function. The DSDD's performance is investigated in GLMM where f is a Bernoulli or Poisson distribution function. The algorithm is also extended to cover GLMM's with zero-inflated data. Goodness-of-fit (GOF) and selection methods for mixed models have been developed in the literature, however their application in models with nonparametric random effects distributions is vague and ad-hoc. Some popular measures such as the Deviance Information Criteria (DIC), conditional Akaike Information Criteria (cAIC) and R2 statistics are potentially useful in this context. Additionally, some cross-validation goodness-of-fit methods popular in Bayesian applications, such as the conditional predictive ordinate (CPO) and numerical posterior predictive checks, can be applied with some minor modifications to suit the non-Bayesian approach. / Graduate / 0463 / rabihsaab@gmail.com

Generalized linear mixture models (GLMM) are widely used in statistical applications to model count and binary data. We consider the problem of nonparametric likelihood estimation of mixing distributions in GLMM's with multiple random effects. The log-likelihood to be maximized has the general form l(G)=Σi log∫f(yi,γ) dG(γ) where f(.,γ) is a parametric family of component densities, yi is the ith observed response dependent variable, and G is a mixing distribution function of the random effects vector γ defined on Ω. The literature presents many algorithms for maximum likelihood estimation (MLE) of G in the univariate random effect case such as the EM algorithm (Laird, 1978), the intra-simplex direction method, ISDM (Lesperance and Kalbfleish, 1992), and vertex exchange method, VEM (Bohning, 1985). In this dissertation, the constrained Newton method (CNM) in Wang (2007), which fits GLMM's with random intercepts only, is extended to fit clustered datasets with multiple random effects. Owing to the general equivalence theorem from the geometry of mixture likelihoods (see Lindsay, 1995), many NPMLE algorithms including CNM and ISDM maximize the directional derivative of the log-likelihood to add potential support points to the mixing distribution G. Our method, Direct Search Directional Derivative (DSDD), uses a directional search method to find local maxima of the multi-dimensional directional derivative function. The DSDD's performance is investigated in GLMM where f is a Bernoulli or Poisson distribution function. The algorithm is also extended to cover GLMM's with zero-inflated data. Goodness-of-fit (GOF) and selection methods for mixed models have been developed in the literature, however their application in models with nonparametric random effects distributions is vague and ad-hoc. Some popular measures such as the Deviance Information Criteria (DIC), conditional Akaike Information Criteria (cAIC) and R2 statistics are potentially useful in this context. Additionally, some cross-validation goodness-of-fit methods popular in Bayesian applications, such as the conditional predictive ordinate (CPO) and numerical posterior predictive checks, can be applied with some minor modifications to suit the non-Bayesian approach. / Graduate / 0463 / rabihsaab@gmail.com

The gravity model for international trade is one of the most successful empirical models in trade literature. There is a long tradition to log-linearise the multiplicative model and to estimate the parameters of interest by least squares. But this practice is inappropriate for several reasons. First of all, bilateral trade flows are frequently zero and disregarding countries that do not trade with each other produces biased results. Second, log-linearisation in the presence of heteroscedasticity leads to inconsistent estimates in general. In recent years, the Poisson gravity model along with pseudo maximum likelihood estimation methods have become popular as a way of dealing with such econometric issues as arise when dealing with origin-destination flows. But the standard Poisson model specification is vulnerable to problems of overdispersion and excess zero flows. To overcome these problems, this paper presents zero-inflated extensions of the Poisson and negative binomial specifications as viable alternatives to both the log-linear and the standard Poisson specifications of the gravity model. The performance of the alternative model specifications is assessed on a real world example, where more than half of country-level trade flows are zero. (authors' abstract) / Series: Working Papers in Regional Science

Savoir comment les patients choisissent les hôpitaux est d'une importance majeure non seulement pour les gestionnaires des hôpitaux mais aussi pour les décideurs. Il s'agit entre autres pour les premiers, de la gestion des flux et l'offre des soins et pour les seconds, l'implémentation des reformes dans le système de santé.Nous proposons dans cette thèse différentes modélisations des données d'admission de patients en fonction de la distance par rapport à un hôpital afin de prévoir le flux des patients et de comparer son attractivité par rapport à d'autres hôpitaux. Par exemple, nous avons utilisé des modèles bayésiens hiérarchiques pour des données de comptage avec possible dépendance spatiale. Des applications on été faites sur des données d'admission de patients dans la région de Languedoc-Roussillon.Nous avons aussi utilisé des modèles de choix discrets tels que les RUMs. Mais vu certaines limites qu'ils présentent pour notre objectif, nous avons relâché l'hypothèse de maximisation d'utilité pour une plus souple et selon laquelle un agent (patient) peut choisir un produit (donc hôpital) dès lors que l'utilité que lui procure ce produit a atteint un certain seuil de satisfaction, en considérant certains aspects. Une illustration de cette approche est faite sur trois hôpitaux de l'Hérault pour les séjours dus à l'asthme en 2009 pour calculer l'envergure territorial d'un hôpital donné . / Understanding how patients choose hospitals is of utmost importance for both hospitals administrators and healthcare decision makers; the formers for patients incoming tide and the laters for regulations.In this thesis, we present different methods of modelling patients admission data in order to forecast patients incoming tide and compare hospitals attractiveness.The two first method use counting data models with possible spatial dependancy. Illustration is done on patients admission data in Languedoc-Roussillon.The third method uses discrete choice models (RUMs). Due to some limitations of these models according to our goal, we introduce a new approach where we released the assumption of utility maximization for an utility-threshold ; that is to say that an agent (patient) can choose an alternative (hospital) since he thinks that he can obtain a certain level of satisfaction of doing so, according to some aspects. Illustration of the approach is done on 2009 asthma admission data in Hérault.

Changes in the abundance and distribution of free (drinking) water can influence wildlife in arid regions. In the western USA, free water is considered by wildlife managers to be important for bighorn sheep (Ovis canadensis), elk (Cervus elaphus), mule deer (Odocoileus hemionus), and pronghorn (Antilocapra americana). Nonetheless, we lack information on the influence of habitat and landscape features surrounding water sources, including wildlife water developments, and how these features may influence use of water by sexes differently. Consequently, a better understanding of differential use of water by the sexes could influence the conservation and management of those ungulates and water resources in their habitats. We deployed remote cameras at water sources to document water source use. For mule deer specifically, we monitored all known water sources on one mountain range in western Utah, during summer from 2007 to 2011 to document frequency and timing of water use, number of water sources used by males and females, and to estimate population size from individually identified mule deer. Male and female mule deer used different water sources but visited that resource at similar frequencies. On average, mule deer used 1.4 water sources and changed water sources once per summer. Additionally, most wildlife water developments were used by both sexes. We also randomly sampled 231 water sources with remote cameras in a clustered-sampling design throughout Utah in 2006 and from 2009 to 2011. In association with camera sampling at water sources, we measured several site and landscape scale features around each water source to identify patterns in ungulate use informative for managers. We used model selection to identify features surrounding water sources that were related to visitation rates for male and female bighorn sheep, elk, mule deer, and pronghorn. Top models for each species were different, but supported models for males and females of the same species generally included similar covariates, although with varying strengths. Our results highlight the differing use of water sources by the sexes. This information will help guide managers when siting and reprovisioning wildlife water developments meant to benefit those species, and when prioritizing natural water sources for preservation or enhancement.

No description available.

The wide application of the genomic microarray technology triggers a tremendous need in the development of the high dimensional genetic data analysis. Many statistical methods for the microarray data analysis consider one gene at a time, but they may miss subtle changes at the single gene level. This limitation may be overcome by considering a set of genes simultaneously where the gene sets are derived from the prior biological knowledge and are called "pathways". We have made contributions on two specific research topics related to the high dimensional genetic pathway data. One is to propose a semi- parametric model for identifying pathways related to the zero inflated clinical outcomes; the other is to propose a multilevel Gaussian graphical model for exploring both pathway and gene level network structures. For the first problem, we develop a semiparametric model via a Bayesian hierarchical framework. We model the pathway effect nonparametrically into a zero inflated Poisson hierarchical regression model with unknown link function. The nonparametric pathway effect is estimated via the kernel machine and the unknown link function is estimated by transforming a mixture of beta cumulative density functions. Our approach provides flexible semiparametric settings to describe the complicated association between gene microarray expressions and the clinical outcomes. The Metropolis-within-Gibbs sampling algorithm and Bayes factor are used to make the statistical inferences. Our simulation results support that the semiparametric approach is more accurate and flexible than the zero inflated Poisson regression with the canonical link function, this is especially true when the number of genes is large. The usefulness of our approaches is demonstrated through its applications to a canine gene expression data set (Enerson et al., 2006). Our approaches can also be applied to other settings where a large number of highly correlated predictors are present. Unlike the first problem, the second one is to take into account that pathways are not independent of each other because of shared genes and interactions among pathways. Multi-pathway analysis has been a challenging problem because of the complex dependence structure among pathways. By considering the dependency among pathways as well as genes within each pathway, we propose a multi-level Gaussian graphical model (MGGM): one level is for pathway network and the second one is for gene network. We develop a multilevel L1 penalized likelihood approach to achieve the sparseness on both levels. We also provide an iterative weighted graphical LASSO algorithm (Guo et al., 2011) for MGGM. Some asymptotic properties of the estimator are also illustrated. Our simulation results support the advantages of our approach; our method estimates the network more accurate on the pathway level, and sparser on the gene level. We also demonstrate usefulness of our approach using the canine genes-pathways data set. / Ph. D.