In an experiment to understand colon carcinogenesis, all animals were exposed to a carcinogen while half the animals were also exposed to radiation. Spatially, we measured the existence of aberrant crypt foci (ACF), namely morphologically changed colonic crypts that are known to be precursors of colon cancer development. The biological question of interest is whether the locations of these ACFs are spatially correlated: if so, this indicates that damage to the colon due to carcinogens and radiation is localized. Statistically, the data take the form of binary outcomes (corresponding to the existence of an ACF) on a regular grid. We develop score??type methods based upon the Matern and conditionally autoregression (CAR) correlation models to test for the spatial correlation in such data, while allowing for nonstationarity. Because of a technical peculiarity of the score??type test, we also develop robust versions of the method. The methods are compared to a generalization of Moran??s test for continuous outcomes, and are shown via simulation to have the potential for increased power. When applied to our data, the methods indicate the existence of spatial correlation, and hence indicate localization of damage. Assuming that there are correlations in the locations of the ACF, the questions are how great are these correlations, and whether the correlation structures di?er when an animal is exposed to radiation. To understand the extent of the correlation, we cast the problem as a spatial binary regression, where binary responses arise from an underlying Gaussian latent process. We model these marginal probabilities of ACF semiparametrically, using ?xed-knot penalized regression splines and single-index models. We ?t the models using pairwise pseudolikelihood methods. Assuming that the underlying latent process is strongly mixing, known to be the case for many Gaussian processes, we prove asymptotic normality of the methods. The penalized regression splines have penalty parameters that must converge to zero asymptotically: we derive rates for these parameters that do and do not lead to an asymptotic bias, and we derive the optimal rate of convergence for them. Finally, we apply the methods to the data from our experiment.

Semiparametric modeling is a hybrid of the parametric and nonparametric modelings where some function forms are known and others are unknown. In this dissertation, we have made several contributions to semiparametric modeling based on the single index model related to the following three topics: the first is to propose a model for detecting change points simultaneously with estimating the unknown function; the second is to develop two models for spatially correlated data; and the third is to further develop two models for spatially-temporally correlated data. To address the first topic, we propose a unified approach in its ability to simultaneously estimate the nonlinear relationship and change points. We propose a single index change point model as our unified approach by adjusting for several other covariates. We nonparametrically estimate the unknown function using kernel smoothing and also provide a permutation based testing procedure to detect multiple change points. We show the asymptotic properties of the permutation testing based procedure. The advantage of our approach is demonstrated using the mortality data of Seoul, Korea from January, 2000 to December, 2007. On the second topic, we propose two semiparametric single index models for spatially correlated data. One additively separates the nonparametric function and spatially correlated random effects, while the other does not separate the nonparametric function and spatially correlated random effects. We estimate these two models using two algorithms based on Markov Chain Expectation Maximization algorithm. Our approaches are compared using simulations, suggesting that the semiparametric single index nonadditive model provides more accurate estimates of spatial correlation. The advantage of our approach is demonstrated using the mortality data of six cities, Korea from January, 2000 to December, 2007. The third topic involves proposing two semiparametric single index models for spatially and temporally correlated data. Our first model has the nonparametric function which can separate from spatially and temporally correlated random effects. We refer it to "semiparametric spatio-temporal separable single index model (SSTS-SIM)", while the second model does not separate the nonparametric function from spatially correlated random effects but separates the time random effects. We refer our second model to "semiparametric nonseparable single index model (SSTN-SIM)". Two algorithms based on Markov Chain Expectation Maximization algorithm are introduced to simultaneously estimate parameters, spatial effects, and times effects. The proposed models are then applied to the mortality data of six major cities in Korea. Our results suggest that SSTN-SIM is more flexible than SSTS-SIM because it can estimate various nonparametric functions while SSTS-SIM enforces the similar nonparametric curves. SSTN-SIM also provides better estimation and prediction. / Ph. D.

影響股票價格之因素，基本上可分為三大類，分別是1.市場因素、2.行業因素、3.公 司因素。本研究主要在了解台灣地區股票投資報酬率與行業因素之關系。 理論基礎部份，介紹單一指數模式(single-indel model)與多重指數模式(multi-in- dex model)，以了解兩模式之主要內容以及有關研究行業因素過程中應注意的問題。 文獻探討部份在說明以往國內外學者之研究大部份證實了行業因素之存在，並敘述將 行業因素引入多重指數模式中，以了解是否能降低殘差相關，提高對股價變動之解釋 能力的有關研究之研究方法與結果。以往國內有關行業因素之研究皆按現行證券市場 之分類標準予以分類，本研究則以陳發輝依六變數( 包括流動比率、負債比率、 EPS 、資產週轉率、資本額、稅後盈餘成長率、交易週轉率) 所區分出之五種類型( 包括 成長績優型、高度投機型、穩健成長型、保守停滯型、穩定獲利型 )來將研究資料中 上市公司予以分類。 研究設計在說明研究的設計過程，包括證券種類的選擇、研究期間、選樣標準、變數 之操作性定義、資料來源、研究步驟等。 實證結果與分析部份對實證研究的統計結果加以解釋並對本研究實證部分的研究限制 加以說明。

This thesis is mainly concerned with the estimation of conditional probabilities in two-way contingency tables, that is probabilities of type P(R=i,S=j|X=x), for (i,j) in {1, . . . , r}×{1, . . . , s}, where R and S are the two categorical variables forming the contingency table, with r and s levels respectively, and X is a vector of explanatory variables possibly associated with R, S, or both. Analyzing such a conditional distribution is often of interest, as this allows to go further than the usual unconditional study of the behavior of the variables R and S. First, one can check an eventual effect of these covariates on the distribution of the individuals through the cells of the table, and second, one can carry out usual analyses of contingency tables, such as independence tests, taking into account, and removing in some sense, this effect. This helps for instance to identify the external factors which could be responsible for an eventual association between R and S. This also gives the possibility to adapt for a possible heterogeneity in the population of interest, when analyzing the table.

Made available in DSpace on 2010-04-20T20:08:13Z (GMT). No. of bitstreams: 0 Previous issue date: 1993-10-13T00:00:00Z / Análise do comportamento dos principais índices de risco no mercado de ações de São Paulo no período de julho de 1984 a junho de 1990. Estudo das condições de diversificação presentes no mercado acionário paulista neste período. Teste do 'Single Index Model' e dos principais modelos de avaliação de ações.