Thesis (Ph.D.)--University of Alberta, 2009. / "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy in English, Department of English and Film Studies." Title from pdf file main screen (viewed on August 24, 2009).

Thesis Ph.D.) - University of Queensland, 2006. / Includes bibliography.

In this work, data quality control and mitigation tools have been developed for improving the accuracy of photovoltaic (PV) system performance assessment. These tools allow to demonstrate the impact of ignoring erroneous or lost data on performance evaluation and fault detection. The work mainly focuses on residential PV systems where monitoring is limited to recording total generation and the lack of meteorological data makes quality control in that area truly challenging. Main quality issues addressed in this work are with regards to wrong system description and missing electrical and/or meteorological data in monitoring. An automatic detection of wrong input information such as system nominal capacity and azimuth is developed, based on statistical distributions of annual figures of PV system performance ratio (PR) and final yield. This approach is specifically useful in carrying out PV fleet analyses where only monthly or annual energy outputs are available. The evaluation is carried out based on synthetic weather data which is obtained by interpolating from a network of about 80 meteorological monitoring stations operated by the UK Meteorological Office. The procedures are used on a large PV domestic dataset, obtained by a social housing organisation, where a significant number of cases with wrong input information are found. Data interruption is identified as another challenge in PV monitoring data, although the effect of this is particularly under-researched in the area of PV. Disregarding missing energy generation data leads to falsely estimated performance figures, which consequently may lead to false alarms on performance and/or the lack of necessary requirements for the financial revenue of a domestic system through the feed-in-tariff scheme. In this work, the effect of missing data is mitigated by applying novel data inference methods based on empirical and artificial neural network approaches, training algorithms and remotely inferred weather data. Various cases of data loss are considered and case studies from the CREST monitoring system and the domestic dataset are used as test cases. When using back-filled energy output, monthly PR estimation yields more accurate results than when including prolonged data gaps in the analysis. Finally, to further discriminate more obscure data from system faults when higher temporal resolution data is available, a remote modelling and failure detection framework is ii developed based on a physical electrical model, remote input weather data and system description extracted from PV module and inverter manufacturer datasheets. The failure detection is based on the analysis of daily profiles and long-term PR comparison of neighbouring PV systems. By employing this tool on various case studies it is seen that undetected wrong data may severely obscure fault detection, affecting PV system s lifetime. Based on the results and conclusions of this work on the employed residential dataset, essential data requirements for domestic PV monitoring are introduced as a potential contribution to existing lessons learnt in PV monitoring.

Missing children is one of the horrors that we are confronted with in today's society. The case study method, a feminist co-search methodology, is used to give a missing child's mother the opportunity to tell and re-tell the painful story. During this co-search process the following aspects of doing ethics and pastoral care and counselling with the mother are constantly negotiated. The term "missing child" is defined and the relevance between the distinction of "missing children" and "run-away children" is discussed. Furthermore, this study explores the many diverse practices of narrative pastoral care and counselling with parents of missing children within an economically disadvantaged community. The conceptualisations regarding loss, hope and meaning-making and how these are utilised in the life of a missing child's mother is discussed. / Philosophy, Practical and Systematic Theology / M.Th.

abstract: Missing data are common in psychology research and can lead to bias and reduced power if not properly handled. Multiple imputation is a state-of-the-art missing data method recommended by methodologists. Multiple imputation methods can generally be divided into two broad categories: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution (e.g., multivariate normal). FCS, on the other hand, imputes variables one at a time, drawing missing values from a series of univariate distributions. In the single-level context, these two approaches have been shown to be equivalent with multivariate normal data. However, less is known about the similarities and differences of these two approaches with multilevel data, and the methodological literature provides no insight into the situations under which the approaches would produce identical results. This document examined five multilevel multiple imputation approaches (three JM methods and two FCS methods) that have been proposed in the literature. An analytic section shows that only two of the methods (one JM method and one FCS method) used imputation models equivalent to a two-level joint population model that contained random intercepts and different associations across levels. The other three methods employed imputation models that differed from the population model primarily in their ability to preserve distinct level-1 and level-2 covariances. I verified the analytic work with computer simulations, and the simulation results also showed that imputation models that failed to preserve level-specific covariances produced biased estimates. The studies also highlighted conditions that exacerbated the amount of bias produced (e.g., bias was greater for conditions with small cluster sizes). The analytic work and simulations lead to a number of practical recommendations for researchers. / Dissertation/Thesis / Doctoral Dissertation Psychology 2015

In this thesis, we investigate the model checking problem for parametric regression model with missing response at random and nonignorable missing response. Besides, we also propose a hypothesis-adaptive procedure which is based on the dimension reduction theory. Finally, to extend our methods to missing response situation, we consider the dimension reduction problem with missing response at random. The .rst part of the thesis introduces the model checking for parametric models with response missing at random which is a more general missing mechanism than missing completely at random. Di.erent from existing approaches, two tests have normal distributions as the limiting null distributions no matter whether the inverse probability weight is estimated parametrically or nonparametrically. Thus, p-values can be easily determined. This observation shows that slow convergence rate of non­parametric estimation does not have signi.cant e.ect on the asymptotic behaviours of the tests although it may have impact in .nite sample scenarios. The tests can de­tect the alternatives distinct from the null hypothesis at a nonparametric rate which is an optimal rate for locally smoothing-based methods in this area. Simulation study is carried out to examine the performance of the tests. The tests are also applied to analyze a data set on monozygotic twins for illustration. In the second part of the thesis, we consider model checking for general linear re­gression model with non-ignorable missing response. Based on an exponential tilting model, we .rst propose three estimators for the unknown parameter in the general linear regression model. Three empirical process-based tests are constructed. We discuss the asymptotic properties of the proposed tests under null and local alterna­tive hypothesis with di.erent scenarios. We .nd that these three tests perform the same in the asymptotic sense. Simulation studies are also carried out to assess the performance of our proposed test procedures. In the third part, we revisit traditional local smoothing model checking proce­dures. Noticing that the general nonparametric regression model can be considered as a special multi-index model, we propose an adaptive testing procedure based on the dimension reduction theory. To our surprise, our method can detect local alter­native at faster rate than the traditional optimal rate. The theory indicates that in model checking problem, dimensionality may not have strong impact. Simulations are carried out to examine the performance of our methodology. A real data analysis is conducted for illustration. In the last part, we study the dimension reduction problem with missing response at random. Based on the work in this part, we can extend the adaptive testing pro­cedure introduced in the third part to the missing response situation. When there are many predictors, how to e.ciently impute responses missing at random is an important problem to deal with for regression analysis because this missing mech­anism, unlike missing completely at random, is highly related to high-dimensional predictor vector. In su.cient dimension reduction framework, the fusion-re.nement (FR) method in the literature is a promising approach. To make estimation more accurate and e.cient, two methods are suggested in this paper. Among them, one method uses the observed data to help on missing data generation, and the other one is an ad hoc approach that mainly reduces the dimension in the nonparametric smoothing in data generation. A data-adaptive synthesization of these two methods is also developed. Simulations are conducted to examine their performance and a HIV clinical trial dataset is analysed for illustration. Keywords: Model checking; Inverse probability weight; Non-ignorable missing re­sponse; Adaptive; Central subspace; Dimension reduction; Data-adaptive Synthesiza­tion; Missing recovery; Missing response at random; Multiple imputation.

nÃo hà / A realidade virtual tem aplicaÃÃes em diferentes Ãreas do conhecimento como Engenharia, CiÃncias, Artes, Entretenimento e EducaÃÃo. Neste trabalho, a realidade virtual à usada como apoio no problema de busca de pessoas desaparecidas. A metodologia proposta utiliza a geraÃÃo de personagens virtuais simulando o processo de reproduÃÃo de seres diplÃides, modelando cuidadosamente as caracterÃsticas dos pais da pessoa desaparecida. Os modelos paternos sÃo gerados na idade do filho no momento do desaparecimento e suas caracterÃsticas genÃticas, sÃo armazenadas em suas estruturas de dados genÃmicas, que serÃo usadas para construir os bancos de gametas masculino e feminino para ser usado em uma fecundaÃÃo simulada. Os descendentes sÃo gerados com a mesma idade da pessoa desaparecida no momento do desaparecimento. AtravÃs de um processo interativo, um modelo plausÃvel da pessoa desaparecida à selecionado entre os descendentes gerados e sua estrutura de dados genÃmica à salva. Os modelos paternos e suas estruturas de dados correspondentes sÃo atualizados atà alcanÃar a idade objetivo (idade na qual se quer projetar a pessoa desaparecida). Em seguida, a estrutura de dados genÃmica da pessoa desaparecida à atualizada com as informaÃÃes contidas nas estruturas de dados paternas jà atualizadas, e um modelo atualizado da pessoa desaparecida à gerado. Este modelo atualizado à um modelo plausÃvel, em que podem ser aplicadas perturbaÃÃes para gerar diversas possibilididades. Os estudos de caso que sÃo apresentados demonstram as potencialidades da metodologia proposta / Virtual reality has applications in different fields of knowledge such as Engineering, Science, Arts, Entertainment and Education. In this work, virtual reality is used to help solving the problem of missing persons. The proposed methodology uses simulated diploid reproduction of virtual characters carefully modeled taking into account the traits of the missing personâs parents. The parents models are generated in the age of son at the moment of disappearance and genetic characteristics of both parents are stored into their genomic data structure, which will be used to construct pools of male and female gametes to be used in a simulated fecundation. The descendants are generated with the same age of the missing person at the time of disappearance. Through an interactive process, a plausible model of the missing person is selected among the generated descendants and its genomic data structure is saved. The parentsâ models and corresponding data structures are updated to reflect the age of the missing person at search time. Next, the genomic data structure of the missing person is updated with the information contained in the updated data structure of the parents, and an updated model of the missing person is generated. This updated model is a plausible model, upon which perturbations can be applied to generate several plausible variants. Case studies are presented that demonstrate the potentials of the proposed methodology

We compare different multiple imputation methods for categorical variables using the MICE package in R. We take a complete data set and remove different levels of missingness and evaluate the imputation methods for each level of missingness. Logistic regression imputation and linear discriminant analysis (LDA) are used for binary variables. Multinomial logit imputation and LDA are used for nominal variables while ordered logit imputation and LDA are used for ordinal variables. After imputation, the regression coefficients, percent deviation index (PDI) values, and relative frequency tables were found for each imputed data set for each level of missingness and compared to the complete corresponding data set. It was found that logistic regression outperformed LDA for binary variables, and LDA outperformed both multinomial logit imputation and ordered logit imputation for nominal and ordered variables. Simulations were ran to confirm the validity of the results.

This thesis concerns doubly robust (DR) estimation in missing data contexts. Previous research is not unanimous as to which estimators perform best and in which situations DR is to be preferred over other estimators. We observe that the conditions surrounding comparisons of DR- and other estimators vary between dierent previous studies. We therefore focus on the effects of three distinct aspects of study design on the performance of one DR-estimator in comparison to outcome regression (OR). These aspects are sample size, the way in which models are misspecified, and the degree of association between the covariates and propensities. We find that while there are no drastic eects of the type of model misspecication, all three aspects do affect how DR compares to OR. The results can be used to better understand the divergent conclusions of previous research.

Studien har utformats från intresset av ett relativt nytt och omtalat fenomen “fear of missing out”, benämnt FoMo. Direkt översatt betyder det rädslan att missa, vilket ofta beskrivs som ett psykologiskt fenomen. Studien har en hermeneutisk ansats och syftar till att förmedla en förståelse av FoMo i relation till sociala aspekter med en socialpsykologisk utgångspunkt. Urvalet består av tio intervjudeltagare i åldern 18–32 år med erfarenhet av upplevd FoMo. Deltagarna har intervjuats om sin upplevelse av FoMo, där de fått tala fritt om fenomenet. Studiens teoretiska referensram bygger på teorier om identitetsutveckling i relation till omvärlden och individens frihet att handla utifrån en kombination av existentialism och symbolisk interaktionism. Resultatet är en tolkning av insamlad data som utformats med stöd av förförståelse, tidigare forskning och den teoretiska referensramen. FoMo handlar om en oro för framtiden. Oron grundar sig i individuella faktorer som är betydande för identitetsutveckling men även sociala faktorer som gemenskap. Att missa en social händelse i sig är inte avgörande för upplevelsen av FoMo utan snarare rädslan för att missa de erfarenheter som händelsen kan leda till. De val vi ständigt måste ta i vardagen har stor betydelse för individen och kan vara avgörande för individens existens.