Pacific silver fir (Abies amabilis (Dougl. ex Loud.) Forbes) is an important timber crop species in coastal forests of B.C. Its range extends from sea-level to almost timberline, and from the hypermaritime region on the west coast of Vancouver Island to the subcontinental region on the leeward side of the Coast Mountains. With this relatively wide climatic amplitude, a large variability in the height growth pattern of Pacific silver fir can be expected, since climate is considered to be the most influential determinant of the trajectory of height over age of forest trees. This variability, however, is not reflected in the height growth curves and site index tables used to estimate Pacific silver fir site index, since the curves and tables were developed from low-elevation stands on Vancouver Island. Consequently, when these curves and tables are applied to high-elevation or submaritime stands, we get biased estimates of site index. Accurate estimates of site index are necessary for accurate yield predictions. Furthermore, they are essential for making rational decisions about whether to cut the forest in situations where potential tree growth is marginal, such as in high-elevation forests.

Western larch (Larix occidentalis Nutt.) is a locally important species in the Nelson Forest Region, and to a lesser extent, in the Kamloops Forest Region. Its range extends from west of the Rockies to Okanagan Lake, and north to Salmon Arm, in the IDF, ICH, MS, and ESSF biogeoclimatic zones. Prior to this study, the site index curves developed for western larch in western Montana were used to model height and estimate site index in British Columbia. It has been suggested that these curves may not adequately reflect the height growth patterns of western larch in BC. Differences could arise from genetics, different methods of selecting sample trees, or climatic differences. The objective of this project was to produce accurate height growth models for western larch in BC.

Height growth models of coastal low- and mid-elevation Pacific silver fir, low-elevation white spruce, and low- and midelevation lodgepole pine have been used for predicting productivity of subalpine fir, Engelmann spruce, and lodgepole pine, respectively. These models, however, are biased in predicting height growth of high-elevation subalpine fir, Engelmann spruce, and lodgepole pine. To improve this situation, 329 sample plots (165 for subalpine fir, 90 for Engelmann spruce, and 74 for lodgepole pine) were located throughout the Engelmann Spruce-Subalpine Fir (ESSF) zone. Stem analysis was carried out on three dominant trees in each 0.04 ha sample plot. For each study species, a height growth model was developed on the data from two-thirds of the sample plots using the conditioned Chapman-Richards’ function; the model was validated using the remaining one-third of the sample plots.

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.

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.

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