archives@tulane.edu / 1 / Melina Calmon Silva

Thesis (M.A.) - Simon Fraser University, 2005. / Theses (School of Criminology) / Simon Fraser University. Also issued in digital format and available on the World Wide Web.

Thesis (M.A.) - Carleton University, 2006. / Includes bibliographical references (p. 154-168). Also available in electronic format on the Internet.

No description available.

Netgi tobuliausiai suplanuotame tyrime atsiranda įvairių rūšių klaidų, dėl kurių gali būti gauti nepatikimi ar nepakankamai tikslūs tyrimo rezultatai, taigi labai svarbu kiek įmanoma labiau sumažinti tų klaidų įtaką tyrimo rezultatams – sumų, vidurkių, santykių įvertiniams. Vienas iš galimų statistinio tyrimo klaidų tipų yra klaidos dėl neatsakymo į apklausą. Jos atsiranda tuomet, kai atsakytojas neatsako į vieną ar kelis klausimyno klausimus. Neatsakymai tyrimuose pasitaiko dėl įvairių priežasčių. Jie iššaukia standartinių įvertinių, kuriuose neatsižvelgiama į neatsakymus, nuokrypį nuo tikrųjų mus dominančių reikšmių, o taip pat šių įvertinių dispersijos padidėjimą. Dabartinėje praktikoje neatsakymai į apklausą nagrinėjami dviem požiūriais: visų pirma bandoma išvengti arba sumažinti neatsakymų lygį. Yra nemažai literatūros ir metodologinės medžiagos tyrinėjančios neatsakymų priežastis bei pateikiančios rekomendacijas kaip sumažinti neatsakymų lygį, tačiau, kai tyrime jau yra neatsakymų, dominančius įvertinius reikia sukonstruoti taip, kad tyrimo rezultatai būtų kuo tikslesni. Neatsakymų sukeliamiems tyrimo rezultatų nuokrypiams sumažinti naudojami įvairūs būdai. Vienas tokių metodų yra praleistų reikšmių įrašymas. Įrašymas – tai trūkstamų duomenų užpildymo būdas, kuris yra labai naudingas analizuojant nepilnas duomenų sekas. Jis išsprendžia duomenų trūkumo problemą duomenų analizės pradžioje. Praleistų reikšmių įrašymo metodika šiuo metu sparčiai vystosi, galima rasti... [toliau žr. visą tekstą] / Nonresponse has been a matter of concern for several decades in survey theory and practice. The problem can be viewed from two different angles: the prevention or avoidance of nonresponse before it occurs, and the special estimation techniques when nonresponse has occurred. The objective of this work is to describe main methods of estimation when nonresponse occurs. Special attention is drawn on one nonresponse estimation method – imputation. Imputation is the procedure when missing values for one or more study variables are “filled in” with substitutes constructed according to some rules, or observed values for elements other than nonrespondents. In this work imputation methods based on some of the more commonly used statistical rules are considered. Some of them are tested on data set having the same distribution as the data of the real survey taken in Statistics Lithuania. The imputation methods are compared with each other and the best imputation method for this data set is picked up. Special attention is paid on regression imputation.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaves 66-72). Also available via the Internet.

Thesis (Ph. D.)--Florida State University, 2005. / Advisor: Daniel McGee, Sr. , Florida State University, College of Arts and Sciences, Dept. of Statistics. Title and description from dissertation home page (viewed Jan. 24, 2006). Document formatted into pages; contains xi, 124 pages. Includes bibliographical references.

Thesis (M.S. in Joint Campaign Planning and Strategy)--Joint Forces Staff College, Joint Advanced Warfighting School, 2005. / "13 May 05." Electronic version of original print document. Includes bibliographical references (p. 75-81).

D.Phil. (Electrical and Electronic Engineering) / The ubiquitous missing data and its pervasiveness in large scale datasets (such as insurance datasets) have inspired research conducted on this thesis to focus on techniques that sustain high accuracies and robustness. It is a consensus in research and in practice that missing data reduces the quality of data and negatively affects the accuracy in classification. The increase in pervasiveness of missing data affects the accuracy and robustness (or resilience) of classifiers. This effectively impacts decision making and calculation of premiums. The goal of the thesis is to present methods that will improve the accuracy and/or robustness of classifiers in the presence of missing data in insurance datasets. The first contribution in this thesis is a comprehensive comparative study of machine learning techniques (classifiers) in the presence of increasing missing data. The study explores and scrutinises their performance and robustness. The classifiers are the repeated incremental pruning to produce error reduction (RIPPER), naïve Bayes (NB), k-nearest neighbour (k-NN), logistic discriminant analysis (LgDA) and support vector machines (SVM). The study reveals that the sensitivity of the classifiers decreases with increasing missing data rate. The RIPPER shows better performance overall, whilst the NB shows better robustness as the quality of the data deteriorates. A second contribution presented in this thesis is a novel relevance determination (ARD) ensemble for effective attribute selection in insurance datasets with large number of attributes and contains missing data. ARD ensemble applies the Bayesian neural networks and evidence framework to find and order attributes based on their relevance to the target outcome. The data is partitioned into numerical and nominal subsets. Each ARD in the ensemble is then constructed using each of the subsets. The combined outcome of each ARD is scrutinised using a confidence factor and the most relevant attributes are selected. Missing data imputation is performed using the mean-mode imputation. The performance of the ARD ensemble is compared to that of the principal component analysis (PCA). The results show that classifiers that use the ARD ensemble achieve high accuracies and sustain robustness than when applied using the PCA.

Missing transverse energy is an important aspect of physics analyses at hadron collider detectors. While other particles can be identified by the energy they deposit in the detector, the presence of neutrinos and other theorized particles must be inferred by an energy imbalance. At the DØ experiment missing energy algorithms exist not only to calculate the missing energy in an event, but to distinguish between possible sources: detector measurement effects or unobserved particles. DØ scientists rely on these algorithms to produce reliable physics results. This thesis presents updates made in the past year to missing energy certification, the unclustered energy resolution, and the missing energy significance calculation. It describes a new processor which calculates missing momentum from tracks as well as development work toward an unclustered energy calibration.