Atkinson 及 Riani 應用前進搜尋演算法來處理百牡利資料中所包含的多重離群值(2001）。在這篇論文中，我們沿用相同的想法來處理在不完整資料下一般線性模型中的多重離群值。這個演算法藉由先填補資料中遺漏的部分，再利用前進搜尋演算法來確認資料中的離群值。我們所提出的方法可以解決處理多重離群值時常會遇到的遮蓋效應。我們應用了一些真實資料來說明這個演算法並得到令人滿意結果。 / Atkinson and Riani (2001) apply the forward search algorithm to deal with the problem of the detection of multiple outliers in binomial data. In this thesis, we extend the similar idea to identify multiple outliers for the generalized linear models when part of data are missing. The algorithm starts with imputation method to fill-in the missing observations in the data, and then use the forward search algorithm to confirm outliers. The proposed method can overcome the masking effect, which commonly occurs when multiple outliers exit in the data. Real data are used to illustrate the procedure, and satisfactory results are obtained.

The problem of statistical calibration of a measuring instrument can be framed both in a statistical context as well as in an engineering context. In the first, the problem is dealt with by distinguishing between the "classical" approach and the "inverse" regression approach. Both of these models are static models and are used to estimate "exact" measurements from measurements that are affected by error. In the engineering context, the variables of interest are considered to be taken at the time at which you observe the measurement. The Bayesian time series analysis method of Dynamic Linear Models (DLM) can be used to monitor the evolution of the measures, thus introducing a dynamic approach to statistical calibration. The research presented employs the use of Bayesian methodology to perform statistical calibration. The DLM framework is used to capture the time-varying parameters that may be changing or drifting over time. Dynamic based approaches to the linear, nonlinear, and multivariate calibration problem are presented in this dissertation. Simulation studies are conducted where the dynamic models are compared to some well known "static'" calibration approaches in the literature from both the frequentist and Bayesian perspectives. Applications to microwave radiometry are given.

Abstract In a two-level hierarchical linear model(HLM2), the outcome as well as covariates may have missing values at any of the levels. One way to analyze all available data in the model is to estimate a multivariate normal joint distribution of variables, including the outcome, subject to missingness conditional on covariates completely observed by maximum likelihood(ML); draw multiple imputation (MI) of missing values given the estimated joint model; and analyze the hierarchical model given the MI [1,2]. The assumption is data missing at random (MAR). While this method yields efficient estimation of the hierarchical model, it often estimates the model given discrete missing data that is handled under multivariate normality. In this thesis, we evaluate how robust it is to estimate a hierarchical linear model given discrete missing data by the method. We simulate incompletely observed data from a series of hierarchical linear models given discrete covariates MAR, estimate the models by the method, and assess the sensitivity of handling discrete missing data under the multivariate normal joint distribution by computing bias, root mean squared error, standard error, and coverage probability in the estimated hierarchical linear models via a series of simulation studies. We want to achieve the following aim: Evaluate the performance of the method handling binary covariates MAR. We let the missing patterns of level-1 and -2 binary covariates depend on completely observed variables and assess how the method handles binary missing data given different values of success probabilities and missing rates. Based on the simulation results, the missing data analysis is robust under certain parameter settings. Efficient analysis performs very well for estimation of level-1 fixed and random effects across varying success probabilities and missing rates. MAR estimation of level-2 binary covariate is not well estimated when the missing rate in level-2 binary covariate is greater than 10%. The rest of the thesis is organized as follows: Section 1 introduces the background information including conventional methods for hierarchical missing data analysis, different missing data mechanisms, and the innovation and significance of this study. Section 2 explains the efficient missing data method. Section 3 represents the sensitivity analysis of the missing data method and explain how we carry out the simulation study using SAS, software package HLM7, and R. Section 4 illustrates the results and useful recommendations for researchers who want to use the missing data method for binary covariates MAR in HLM2. Section 5 presents an illustrative analysis National Growth of Health Study (NGHS) by the missing data method. The thesis ends with a list of useful references that will guide the future study and simulation codes we used.

In this thesis we describe the theory of generalized linear models and demon- strate its applications in non-life insurance. We also introduce some methods com- monly used to estimation of regression parameters and hypothesis testing . Further- more, we discuss possible extensions of GLM by introducing tools for reparametriza- tion of predictors which leads to new classes of models, concretely to segmented generalized linear models and generalized additive models. Consequently, we derive models appropriate for actuarial praxis using the real insurance data. In practical part of this thesis we illustrate the use of appropriate software for calculating the parameters of GAM and find way how to use open source statistical program .

The aim of this diploma thesis is statistical processing of a sample of patients that have been hospitalized and treated for ureteropelvic junction obstruction at the urological department of ÚNV Prague in last 20 years and to determine the optimal treatment method. Evaluation of surgical techniques from the surgical and economical point of creates a comprehensive image of advantages and disadvantages connected with application of a particular method and enables all participating subjects to decide in case of doubt. In this case the statistical analysis is a proper instrument, leading to find answers, however, it also gives an opportunity for discussion.

This study aimed to investigate the relationship between psychological capital and the work-related attitudes of job satisfaction and organisational commitment recognising the hierarchical nature of the data. This relationship was examined in light of a supportive organisational climate as defined by supervisor support which played the role of a moderator in this relationship. Data was gathered using a number of structured questionnaires which were distributed to employees via an online link. The Psychological Capital Questionnaire (Luthans, Youssef & Avolio, 2007), Organisational Commitment Questionnaire (Mowday, Steers & Porter, 1982), Warr, Cook and Wall’s (1979) measure of job satisfaction and Eisenberger’s (1986) adapted measure of supervisor support were administered. A total of 14 departments participated in the study and 50 employees completed the questionnaires. A Hierarchical Linear Model analysis (HLM) was used to analyse the data along with Pearson product moment correlations and a two-way ANOVA. Results indicated that psychological capital was related moderately and positively to job satisfaction but was not related to organisational commitment. Supervisor support was related to both job satisfaction and organisational commitment. Finally supervisor support moderated the relationship between psychological capital and job satisfaction but no interaction was found for the relationship between psychological capital and organisational commitment as moderated by supervisor support. This paper concludes with a discussion of the results, implications of the findings, limitations and directions for future research.

nÃo hà / O presente estudo teve como objetivo identificar nos resultados do SAEB/2003, no CearÃ, um modelo multilinear com variÃveis que garantam o maior grau de explicaÃÃo do rendimento em matemÃtica nos testes aplicados nas amostras de 4 sÃrie da escola pÃblica; 4 sÃrie da escola particular; 8 sÃrie da escola pÃblica e 8 sÃrie da escola particular. Tomando-se, entre as variÃveis do diretor, do professor, do aluno e das condiÃÃes fÃsicas da escola, as variÃveis que se relacionavam significativamente com o rendimento no teste de matemÃtica em cada uma das amostraS, com os recursos do software SPSS (Statical Package for the Social Sciences), uma regressÃo linear em cada amostra, com o mÃtodo âstepwareâ. Obtiveram-se quatro modelos lineares mÃltiplos com variÃveis que permitem um maior grau de explicaÃÃo do rendimento no teste em matemÃtica em cada uma das amostras. Conclui-se que a anÃlise desse tipo permite a formulaÃÃo de polÃticas pÃblicas capazes de superar a busca da melhoria do ensino por intervenÃÃes pontuais. / This study had as its objective the identification in the results of SAEB/2003, here in CearÃ, of a multilinear model with variables that gave a greater degree of explaination for the results in mathematics of tests given in the randomic sample of the fourth years students in public schools, the fourth year students in private schools, the eight year students in public schools and the eight year students in private schools. The following variables were taken into account: the director, the teacher, the student, the physical state of the school as variables that had significant relevance with the results of the tests in mathematics in each one of the samples, using the resources of the software SPSS (Statical Package for the School Sciences), a linear regression in each sample, using the method "stepware". Four multilinear models with variables were obtained that permitted a greater degree of explaination for the results of the mathematic tests in each sample. The study concluded affirming that an analysis of this type permits the formulation of public politics capable of overcoming the search for better forms of teaching with punctual interventions.

The world is experiencing yet another energy- and fuel predicament as oil prices are escalating to new hights. Alternative fuels are being promoted globally as the increasing gasoline prices trigger inflation. Basic food commodities are some of the goods hit by this inflation and the purpose of this thesis is to analyse whether the higher maize and sugar prices are having any effect on the expanding ethanol production. This thesis focuses on the two major crop inputs in ethanol production: maize (in the US) and sugar cane (in Brazil). Econometric tests using cross-sectional data were carried through to find the elasticities of the variables. The crops prices were tested against ethanol output using the log-linear model in several regressions to find a relationship. In addition, the output levels of the crops were tested using the same method. It was found that maize prices and output affects ethanol production. Sugar cane prices do not have any significant impact on ethanol production while sugar cane output has a small, yet significant relationhip with ethanol. Consequently, ethanol’s rise in the fuel market could be a result of increased maize input, rather than sugar. / Dagens värld upplever ännu ett energi- och bränsle predikament när oljepriser eskalerar mot nya höjder. Alternativa bränslen marknadsförs globalt samtidigt som de stigande bensinpriserna stimulerar inflationen. Några av de varor som drabbas av denna inflation är grundläggande livsmedelsprodukter och syftet med denna uppsats är att analysera huruvida de högre priserna på majs och socker påverkar den expanderande etanolproduktionen. Uppsatsen fokuserar på de två stora grödor som används som insatsvaror vid framställningen av etanol: majs (i USA) och sockerrör (i Brasilien). Ekonometriska tester genomfördes för att erhålla variablernas elasticiteter med hjälp av den cross-sectional data som behandlades. Genom log-linear modellen utfördes det ett antal regressioner för att hitta ett samband mellan grödornas priser och etanolproduktionen. Därutöver genomfördes tester för att hitta sambandet mellan grödornas utbud och etanol med hjälp av samma modell. Det upptäcktes att både pris och utbudet av majs påverkar etanolproduktionen. Sockerrörspriser har ingen signifikant inverkan på etanolproduktionen medan utbudet av sockerrör har en signifikant, om än svag, relation till etanol. Följaktligen kan etanols tillväxt i  bränslemarknaden tolkas som ett resultat av en stigande majsinsats snarare än sockerinstats vid etanolframställningen.

Forest management contributes to the changes in forest structure by turning heterogenous forests of varied age into homogenous forests of similar age and thus affect bird species depending on different structures or habitats which are lost during forestry. In this report, a study was made to investigate how the amount of decidious trees, dead wood and variation in tree diameter affect bird diversity. The purpose of this study was to be able to give forest management guidelines to increase bird diversity. This study was conducted by investigating 65 transects in forests of different structure south of Linköping, Sweden. Along the 65 transects, birds were inventoried as well as the vegetation. The trees were measured in 5 circles along each transect. The data from the investigations both on birds and vegetation were analysed by using generalized linear models. The results showed that amount of deadwood and variation in tree diameter had the strongest effects on bird diversity, and to some extent the amount of decidious trees. By applying this knowledge of the positivt effects on birds when increasning the amount of deadwood, decidious trees and variation in tree diameter in the forests, it is possible to create better conditions for maintaining species richness and diversity.

Most studies of gustatory coding have been performed in either anesthetized or awake, passively stimulated rats. In this dissertation the influences of behavioral state on gustatory processing in awake rats are described. In the first set of experiments, the effects of non-contingent tastant delivery on the chemical tuning of single neurons were explored. Tastants were delivered non-contingently through intra-oral cannulas to restrained, non water-deprived rats while single unit responses were recorded from the gustatory cortex (GC). As the subjects' behavior progressed from acceptance to rejection of the tastants, the chemical tuning of the neurons changed as well. This suggests that the subjects' behavioral state powerfully influences gustatory processing. In the second set of experiments, rats were trained to lick for fluid reinforcement on an FR5 schedule while single unit activity was recorded from GC. In this case, the chemical tuning was much more stable. Under this paradigm, chemosensory responses were rapid (~ 150 ms) and broadly tuned. In the third study, it was found that ensembles of GC neurons could discriminate between tastants and their concentrations on a single trial basis, and such discrimination was accomplished with a combination of rate and temporal coding. Ensembles of GC neurons also anticipated the identity of the upcoming stimulus when the tastant delivery was predictable. Finally, it was found that ensembles of GC neurons could discriminate between the bitter stimuli nicotine and quinine. Nicotine is both a bitter tastant and a trigeminal stimulant, and when the acetylcholine receptors in the lingual epithelium were blocked with mecamylamine, the ensembles failed to discriminate nicotine from quinine.