Validity generalization work over the past 25 years has called into question the veracity of the assumption that validity is situationally specific. Recent theoretical and methodological work has suggested that validity coefficients may be transportable even if true validity is not a constant. Most transportability work is based on the assumption that the distribution of rho ( ρi ) is normal, yet, no empirical evidence exists to support this assumption. The present study used a competing model approach in which a new procedure for assessing transportability was compared with two more commonly used methods. Empirical Bayes estimation (Brannick, 2001; Brannick & Hall, 2003) was evaluated alongside both the Schmidt-Hunter multiplicative model (Hunter & Schmidt, 1990) and a corrected Hedges-Vevea (see Hall & Brannick, 2002; Hedges & Vevea, 1998) model. The purpose of the present study was two-fold. The first part of the study compared the accuracy of estimates of the mean, standard deviation, and the lower bound of 90 and 99 percent credibility intervals computed from the three different methods across 32 simulated conditions. The mean, variance, and shape of the distribution varied across the simulated conditions. The second part of the study involved comparing results of analyses of the three methods based on previously published validity coefficients. The second part of the study was used to show whether choice of method for determining whether transportability is warranted matters in practice. Results of the simulation analyses suggest that the Schmidt-Hunter method is superior to the other methods even when the distribution of true validity parameters violates the assumption of normality. Results of analyses conducted on real data show trends consistent with those evident in the analyses of the simulated data. Conclusions regarding transportability, however, did not change as a function of method used for any of the real data sets. Limitations of the present study as well as recommendations for practice and future research are provided.

Includes vita. / Title from PDF of title page. / Document formatted into pages; contains 134 pages. / Thesis (Ph.D.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: Validity generalization work over the past 25 years has called into question the veracity of the assumption that validity is situationally specific. Recent theoretical and methodological work has suggested that validity coefficients may be transportable even if true validity is not a constant. Most transportability work is based on the assumption that the distribution of rho ( ) is normal, yet, no empirical evidence exists to support this assumption. The present study used a competing model approach in which a new procedure for assessing transportability was compared with two more commonly used methods. Empirical Bayes estimation (Brannick, 2001; Brannick & Hall, 2003) was evaluated alongside both the Schmidt-Hunter multiplicative model (Hunter & Schmidt, 1990) and a corrected Hedges-Vevea (see Hall & Brannick, 2002; Hedges & Vevea, 1998) model. The purpose of the present study was two-fold. The first part of the study compared the accuracy of estimates of the mean, standard deviation, and the lower bound of 90 and 99 percent credibility intervals computed from the three different methods across 32 simulated conditions. The mean, variance, and shape of the distribution varied across the simulated conditions. The second part of the study involved comparing results of analyses of the three methods based on previously published validity coefficients. The second part of the study was used to show whether choice of method for determining whether transportability is warranted matters in practice. Results of the simulation analyses suggest that the Schmidt-Hunter method is superior to the other methods even when the distribution of true validity parameters violates the assumption of normality. Results of analyses conducted on real data show trends consistent with those evident in the analyses of the simulated data. Conclusions regarding transportability, however, did not change as a function of method used for any of the real data sets. Limitations of the present study as well as recommendations for practice and future research are provided. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

This thesis is focused on object tracking by a decentralized sensor network using fusion center-based and consensus-based distributed particle filters. The model includes clutter as well as missed detections of the object. The approach uses sparsity of global likelihood function, which, by means of appropriate sparse approximation and the suitable dictionaty selection can significantly reduce communication requirements in the decentralized sensor network. The master's thesis contains a design of exchange methods of sparse data in the sensor network and a comparison of the proposed methods in terms of accuracy and energy requirements.

Model-based estimation techniques have been widely used in small area estimation. This thesis focuses on the Hierarchical Bayes (HB) estimation techniques in application to small area estimation for survey data. We will study the impact of applying spatial structure to area-specific effects and utilizing a specific generalized linear mixed model in comparison with a traditional Fay-Herriot estimation model. We will also analyze different loss functions with applications to a small area estimation problem and compare estimates obtained under these loss functions. Overall, for the case study under consideration, area-specific geographical effects will be shown to have a significant effect on estimates. As well, using a generalized linear mixed model will prove to be more advantageous than the usual Fay-Herriot model. We will also demonstrate the benefits of using a weighted balanced-type loss function for the purpose of balancing the precision of estimates with their closeness to the direct estimates.

Model-based estimation techniques have been widely used in small area estimation. This thesis focuses on the Hierarchical Bayes (HB) estimation techniques in application to small area estimation for survey data. We will study the impact of applying spatial structure to area-specific effects and utilizing a specific generalized linear mixed model in comparison with a traditional Fay-Herriot estimation model. We will also analyze different loss functions with applications to a small area estimation problem and compare estimates obtained under these loss functions. Overall, for the case study under consideration, area-specific geographical effects will be shown to have a significant effect on estimates. As well, using a generalized linear mixed model will prove to be more advantageous than the usual Fay-Herriot model. We will also demonstrate the benefits of using a weighted balanced-type loss function for the purpose of balancing the precision of estimates with their closeness to the direct estimates.

Αρχικά γίνεται μια ιστορική αναδρομή, μια παρουσίαση της διπαραμετρικής Γενικευμένης εκθετικής κατανομής (τύπος κατανομής, συνάρτηση πυκνότητας πιθανότητας κλπ) και αναφέρονται βασικά χαρακτηριστικά της κατανομής. Στη συνέχεια αναφέρονται βασικοί ορισμοί και θεωρήματα σχετικά κυρίως με τη σημειακή παραμετρική εκτίμηση καθώς και την εκτίμηση κατά Bayes. Το επόμενο κεφάλαιο πραγματεύεται την ανάλυση του μοντέλου και τις βασικές ιδιότητες της Γενικευμένης εκθετικής κατανομής. Επίσης μελετώνται ειδικά θέματα, όπως συναρτήσεις επιβίωσης, πληροφορία Fisher, διατεταγμένες παρατηρήσεις, κατανομή του αθροίσματος και παραγωγή τυχαίων αριθμών, στα πλαίσια της Γενικευμένης εκθετικής κατανομής. Στη συνέχεια αναλύονται και εφαρμόζονται μέθοδοι σημειακής εκτίμησης (Μέγιστη Πιθανοφάνεια, Μέθοδος ροπών, Μέθοδος εκατοστημορίων, Ελάχιστα και σταθμισμένα ελάχιστα Τετράγωνα, L-ροπές) για την εκτίμηση των παραμέτρων της κατανομής. Μελετάται και η απόδοση των εκτιμητών για τις διάφορες μεθόδους εκτίμησης. Ακολουθεί η εκτίμηση τύπου Bayes των παραμέτρων (με συναρτήσεις ζημίας τετραγωνικού σφάλματος και LINEX αντίστοιχα). Αναφέρονται πάλι συμπεράσματα για την απόδοση των εκτιμητών και σύγκριση με τους εκτιμητές μέγιστης πιθανοφάνειας. Τελικά παρουσιάζουμε την προσέγγιση ενός αναλογιστικού πίνακα μέσω της Γενικευμένης εκθετικής κατανομής. / In the beginning, we mention a historical recursion, a presentation of the 2-parameter Generalized exponential distribution ( distribution type, probability density function etc.) and we also mention basic characteristics of the distribution. Basic definitions and theorems about point estimation and Bayes estimation are reported. Furthermore, we discource on the analysis of the model and basic properties of the Generalized exponential distribution. Special themes, such as survival functions, Fisher information, order statistics, sum distribution and production of random numbers are analyzed in the frame of the Generalized exponential distribution. Moreover, we analyze and apply point estimation methods (maximum likelihood, method of moments, percentile estimation, least (and weighted least) squares, method of L-moments) in order to estimate parameters of the distribution. Performance of the estimators for different estimation methods is also analyzed. Next, bayesian estimation of the parameters (under squared error loss function and LINEX loss function) is coming up for discussion. We also analyze the performance of the estimators and compare them to the maximum likelihood estimators. Finally, we present approximation of an actuarial table via Generalized exponential distribution.

Les travaux présentés dans cette thèse concernent l'analyse statistique de cas particuliers du processus de Cox. Dans une première partie, nous proposons une synthèse des résultats existants sur le processus power-law (processus d'intensité puissance), synthèse qui ne peut être exhaustive étant donné la popularité de ce processus. Nous considérons une approche bayésienne pour l'inférence des paramètres de ce processus qui nous conduit à introduire et à étudier en détails une distribution que nous appelons loi H-B. Cette loi est une loi conjuguée. Nous proposons des stratégies d'élicitation des hyperparamètres et étudions le comportement des estimateurs de Bayes par des simulations. Dans un deuxième temps, nous étendons ces travaux au cas du processus d’intensité exponentielle (exponential-law process). De la même façon, nous définissons et étudions une loi conjuguée pour l'analyse bayésienne de ce dernier. Dans la dernière partie de la thèse, nous considérons un processus auto-excité qui intègre une covariable. Ce travail est motivé, à l'origine, par un problème de fiabilité qui concerne des données de défaillances de matériels exposés à des environnements sévères. Les résultats sont illustrés par des applications sur des données d'activités orageuses collectées dans deux départements français. Enfin, nous donnons quelques directions de travail et perspectives de futurs développements de l'ensemble de nos travaux. / The work presented in this PhD dissertation concerns the statistical analysis of some particular cases of the Cox process. In a first part, we study the power-law process (PLP). Since the literature for the PLP is abundant, we suggest a state-of-art for the process. We consider the classical approach and recall some important properties of the maximum likelihood estimators. Then we investigate a Bayesian approach with noninformative priors and conjugate priors considering different parametrizations and scenarios of prior guesses. That leads us to define a family of distributions that we name H-B distribution as the natural conjugate priors for the PLP. Bayesian analysis with the conjugate priors are conducted via a simulation study and an application on real data. In a second part, we study the exponential-law process (ELP). We review the maximum likelihood techniques. For Bayesian analysis of the ELP, we define conjugate priors: the modified- Gumbel distribution and Gamma-modified-Gumbel distribution. We conduct a simulation study to compare maximum likelihood estimates and Bayesian estimates. In the third part, we investigate self-exciting point processes and we integrate a power-law covariate model to this intensity of this process. A maximum likelihood procedure for the model is proposed and the Bayesian approach is suggested. Lastly, we present an application on thunderstorm data collected in two French regions. We consider a strategy to define a thunderstorm as a temporal process associated with the charges in a particular location. Some selected thunderstorms are analyzed. We propose a reduced maximum likelihood procedure to estimate the parameters of the Hawkes process. Then we fit some thunderstorms to the power-law covariate self-exciting point process taking into account the associated charges. In conclusion, we give some perspectives for further work.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

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Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal