FACTORS INFLUENCING RATES OF ADOPTION OF TRICHOMONIASIS VACCINE BY NEVADA RANGE CATTLE PRODUCERS

Tritrichimonas foetus vaccine has been marketed since 1989 to combat the trichomoniasis disease that causes reproductive failure and considerable economic loss to Nevada ranchers. An ex post technology adoption model is estimated to examine the possible adoption rate, to identify the factors that may influence the adoption behavior, and to test how the probability of adoption for five possible adopter groups would change due to changes in various ranch specific factors. Results indicate that use of computers, veterinary checkup of herd, and herd size influence the probability of adoption. Model results show that cooperative extension programs enhance the rate of adoption.Livestock Production/Industries,

Why do firms opt for Alternative-Format Financial Statements ? Some Evidence from France

Historically, the format of financial statements has varied from one country to another. Recently, due to the attractiveness of their capital markets, the strength of their accounting professions and the influence of their institutional investors, Anglo-American countries have seen the impact of their accounting practices on other nations increase steadily, even influencing the actual format of financial statements. Given that French accounting regulations allow a certain degree of choice in consolidated balance sheet format (‘by nature’ or ‘by term’) and income statement format (‘by nature’ or ‘by function’), this study examines a sample of 199 large French listed firms in an attempt to understand why some of these firms do not use the traditional French formats (‘by nature’ for the balance sheet and ‘by nature” for the income statement), instead preferring Anglo-American practices (‘by term’ format for the balance sheet and ‘by function’ format for the income statement). We first analyze the balance sheet and income statement formats separately using a logit model, then combine the two and enrich the research design with a generalized ordered logit model and a multinomial logit regression. Our results confirm that the major driving factor behind the adoption of one or two alternative formats is the firm’s degree of internationalization, not only financial (auditor type, foreign listing and the decision to apply alternative accounting standards) but also commercial (company size and the internationalization of sales).Disclosure; Determinants; Financial Statements; Alternative format; France; Logit; Generalized ordered logit; Multinomial logit

Statistical analysis of ordered categorical data via a structural heteroskedastic threshold model

In the standard threshold model, differences among statistical subpopulations in the distribution of ordered polychotomous responses are modeled via differences in location parameters of an underlying normal scale. A new model is proposed whereby subpopulations can also differ in dispersion (scaling) parameters. Heterogeneity in such parameters is described using a structural linear model and a loglink function involving continuous or discrete covariates. Inference (estimation, testing procedures, goodness of fit) about parameters in fixed-effects models is based on likelihood procedures. Bayesian techniques are also described to deal with mixed-effects model structures. An application to calving ease scores in the US Simmental breed is presented; the heteroskedastic threshold model had a better goodness of fit than the standard one.Dans le modèle à seuils classique, les différences de réponses entre sous-populations selon des catégories discrètes ordonnées sont modélisées par des différences entre paramètres de position mesurés sur une variable normale sous-jacente. L’approche présentée ici suppose que ces sous-populations diffèrent aussi par leurs paramètres de dispersion (ou paramètres d’échelle). L’hétérogénéité de ces paramètres est décrite par un modèle linéaire structurel et une fonction de lien logarithmique impliquant des covariables discrètes ou continues. L’inférence (estimation, qualité d’ajustement, test d’hypothèse) sur les paramètres dans les modèles à effets fixes est basée sur les méthodes du maximum de vraisemblance. Des techniques bayésiennes sont également proposées pour le traitement des modèles linéaires mixtes. Une application aux notes de difficultés de vêlage en race Simmental américaine est présentée. Le modèle à seuils hétéroscédastiqué améliore dans ce cas la qualité de l’ajustement des données par rapport au modèle standard

Statistical Modeling for High-dimensional Compositional data with Applications to the Human Microbiome

Compositional data refer to the data that lie on a simplex, which are common in many scientific domains such as genomics, geology, and economics. As the components in a composition must sum to one, traditional tests based on unconstrained data become inappropriate, and new statistical methods are needed to analyze this special type of data. This dissertation is motivated by some statistical problems arising in the analysis of compositional data. In particular, we focus on the high-dimensional and over-dispersed setting, where the dimensionality of compositions is greater than the sample size and the dispersion parameter is moderate or large. In this dissertation, we consider a general problem of testing for the compositional difference between K populations. We propose a new Bayesian hypothesis, together with a nonparametric and distance-based testing method. Furthermore, we utilize multiple variable-selecting models, including LASSO, elastic net, ridge regression and cumulative logit model, to identify the most important subset of variables. This dissertation is structured as follows: Chapter 1 introduces the compositional microbiome data, and then briefly review different statistical tests and model to be used in our framework, including distance correlation, LASSO, Ridge regression, elastic net, cumulative logit and adjacent-category logit model. Chapter 2 then presents our new statistical test together with two real world applications form human microbiome study. We first formulate a hypothesis from the Bayesian point of view and suggest a nonparametric test based on inter-point distance to evaluate statistical significance. Unlike most existing tests for compositional data, the distance-based method is more sensitive to the compositional difference than the mean-based method, especially when the data are over-dispersed or zero-inflated. It does not rely on any data transformation, sparsity assumption or regularity conditions on the covariance matrix, but directly analyzes the compositions. The performance of this method is evaluated using simulation studies. We apply this new procedure to two human microbiome datasets including a throat microbiome dataset and an intestinal microbiome data. In addition to the overall testing, we also want to identify a small subset of variables that distinguish different populations. Chapter 3 introduces the procedure to select most significant variables (bacteria or genus) using LASSO, Ridge regression, elastic net, cumulative logit model and adjacent-category logit models. Chapter 4 validates our findings from Chapter 3 and presents visualizations using multi-dimensional scaling (MDS). Chapter 5 discusses and concludes the dissertation with some future perspectives

A Battle of Taste and Environmental Convictions for Ecolabeled Seafood: A Choice Experiment

This paper describes a choice experiment addressing preferences for ecolabeled seafood, in which the experimental design allows for choices among various fresh seafood products. The primary emphasis is the potential trade-off between taste (i.e., a favored species) and the presence of an ecolabel, when multiple seafood products are available.Consumer/Household Economics,

Determinants of Private Equity Exit Strategies: An Empirical Study of the Nordic Private Equity Market

This thesis seeks to uncover the determinants of private equity (PE) exit strategies in the Nordics by examining the three most common exit routes available to PE firms: secondary buyouts (SBOs), initial public offerings (IPOs) and trade sales. Based on data received by Argentum, we construct a unique sample containing PE firm and fund characteristics, portfolio company characteristics and market conditions for 525 Nordic buyouts between 2008–2021. We find evidence of PE funds capitalizing on “windows of opportunities” by exiting through IPOs in hot stock markets to cash in on their investments at presumably higher valuations, which is consistent with previous research. Second, we find evidence that the purchasing buyout fund participating in an SBO singles out companies with better operating performance who exceed other companies in coping with higher levels of debt. Third, the probability of exiting through an SBO relative to an IPO tends to increase as the fund approaches maturity, highlighting the attractiveness of an SBO: it often achieves a high price, with low transaction risk and the shortest delay in receiving the proceeds. There is no evidence suggesting that the increasing amount of committed, but unallocated, capital leads to a relative increase in SBOs or that PE funds closer to maturity tend to exit through SBOs when investments are made late in the fund’s life cycle. These two findings are particularly intriguing as it contradicts the claims made by PE critics of asset flipping and SBOs being “pass-the-parcel” deals for managers willing to exploit PE funds’ fee structures. Furthermore, older companies with lower revenues and better asset utilization have a significantly higher probability of being exited through a trade sale, possibly illustrating thirdparty buyers’ preferences in pursuing more mature companies relative to the preferences of PE funds. In line with several studies, we also find that IPOs appear to be the preferred exit choice for PE funds exiting larger portfolio companies. Last, we find no evidence regarding the impact of favorable credit markets or higher information asymmetry on the choice of exit channel.nhhma

Bayesian Analysis of Transition Model for Longitudinal Ordinal Response Data: Application to Insomnia Data

In this paper, we present a Bayesian framework for analyzing longitudinal ordinal response data. In analyzing longitudinal data, the possibility of correlations between responses given by the same individual needs to be taken into account. Various models can be used to handle such correlations such as marginal modeling, random effect modeling and transition (Markov) modeling. Here a transition modeling is used and a Bayesian approach is presented for analyzing longitudinal data. A cumulative logistic regression model and the Bayesian method, using MCMC, are implemented for obtaining the parameters estimates. Our approach is applied on a two-period longitudinal Insomnia data where the Bayesian estimate for measure of association, , between the initial and follow-up ordinal responses is obtained in each level of a treatment variable. Then, the sensitivity of posterior summaries to changes of prior hyperparameters is investigated. We also use Bayes factor criterion for testing some important hypotheses

Statistical Analysis of Correlated Ordinal Data: Application to Cluster Randomization Trials

Cluster randomization trials have become increasingly popular when theoretical, ethical or practical considerations preclude the use of traditional trials that randomize individual subjects. Although some methods for analyzing clustered ordinal data have been brought to wide attention, these are less developed as compared to methods for analyzing clustered continuous or binary outcome data. The aim of this thesis is to refine existing strategies which may be applicable to clustered ordinal data as well as extensions which have been previously considered only for clustered binary responses. The approaches include adjusted Cochran-Armitage tests using an ICC estimator, and correction and modification strategies to improve the small-sample performance of the Wald test and score test in GEE for clustered ordinal data. The type I error and power for these test statistics are investigated using a simulation study. Simulation results show that kappa-type estimators had less bias than ICC estimators when cluster sizes were fixed and small for ρ = 0.005 or ρ = 0.01. Conversely, ANOVA ICCs had relatively smaller bias in the case of variable cluster sizes. In addition, small-sample performance of GEE robust Wald tests are improved by using adjustments and corrections. The adjusted test WBC1 is recommended in terms of type I error and power. The discussion is illustrated using data from a school-based cluster randomization trial