Modelos para dados binários são bastante utilizados em várias situações práticas. Transformações em Análise de Regressão podem ser aplicadas para linearizar ou simplificar o modelo e também para corrigir desvios de suposições. Neste trabalho, descrevemos o uso de transformações nos modelos de regressão logística para dados binários e apresentamos modelos envolvendo parâmetros adicionais de modo a obter um ajuste mais adequado. Posteriormente, analisamos o custo da estimação quando são adicionados parâmetros aos modelos e apresentamos os testes de hipóteses relativos aos parâmetros do modelo de regressão logística de Box-Cox. Finalizando, apresentamos alguns métodos de diagnóstico para avaliar a influência das observações nas estimativas dos parâmetros de transformação da covariável, com aplicação a um conjunto de dados reais. / Binary data models have a lot of utilities in many practical situations. In Regrssion Analisys, transformations can be applied to linearize or simplify the model and correct deviations of the suppositions. In this dissertation, we show the use of the transformations in logistic models to binary data models and models involving additional parameters to obtain more appropriate fits. We also present the cost of the estimation when parameters are added to models, hypothesis tests of the parameters in the Box-Cox logistic regression model and finally, diagnostics methods to evaluate the influence of the observations in the estimation of the transformation covariate parameters with their applications to a real data set.

This dissertation studies household income and consumption. In the first chapter, I identify the causal effect of retirement on health service utilization in China. In the second chapter, I investigates the impact that retirement has on the family support network of “sandwich” generations in China. In the third chapter, I propose a new estimator for linear quantile regression models with generated regressors, and apply it to study Engel curves for various commodity consumption for families in the UK. In the first chapter, I apply a regression discontinuity design by exploiting the exogenous mandatory retirement age rules in China in order to identify the causal effect of retirement on health service utilization. In China, the social insurance Urban Employee Basic Medical Insurance (UEBMI) provision continues after individuals retire. Employees, however, stop paying the premium and enjoy reduced cost sharing after they retire. Individual medical expenses, insurance costs, and benefits are recorded in the China Household Finance Survey 2013 (CHFS). Significantly, males and females respond differently to this decrease in the relative price of health insurance at the time of retirement. Females are generally more willing to increase their out-of-pocket expenditures in order to take advantage of better health insurance benefits and utilize more medical care. Males, by contrast, do not respond to this change in relative price in the same manner. In the second chapter, I investigates the impact that retirement has on the family support networks of “sandwich” generations in China. These middle-aged households have an inter-generational support network that includes both upward transfers (their parents or parents-in-law), as well as downward transfers (their children). I use micro data from CHARLS (China Health and Retirement Longitudinal Study) concerning middle-aged and elderly households in order to evaluate the changes that retirement can have on this family support network, primarily by exploiting the exogenous mandatory retirement age rules in China. I make the identifying assumption that inter-generational transfers would evolve more smoothly if households would not retire and apply a regression discontinuity approach. I find that retirement induces “sandwich” generations to switch roles in the private network as well as in the public transfer channel; indeed, is 55 percentage point more likely that households will switch from resource providers to resource recipients in the channel of private transfers. In addition, these “sandwich” generations are about 47 percentage point more likely to receive money from their non-coresident children when they retire. In the third chapter, we studies estimation and inference for linear quantile regression models with generated regressors. We suggest a practical two-step estimation procedure, where the generated regressors are computed in the first step. The asymptotic properties of the two-step estimator, namely, consistency and asymptotic normality are established. We show that the asymptotic variance-covariance matrix needs to be adjusted to account for the first-step estimation error. We propose a general estimator for the asymptotic variance-covariance, establish its consistency, and develop testing procedures for linear hypotheses in these models. Monte Carlo simulations to evaluate the finite-sample performance of the estimation and inference procedures are provided. Finally, we apply the proposed methods to study Engel curves for various commodities using data from the UK Family Expenditure Survey. We document strong heterogeneity in the estimated Engel curves along the conditional distribution of the budget share of each commodity. The empirical application also emphasizes that correctly estimating confidence intervals for the estimated Engel curves by the proposed estimator is of importance for inference.

Pete Riegel, a well-known sport expert, proposed the formula of race time prediction in 1977. This article discusses whether it is also suitable for Taiwan marathoners. We compiled two hundred and four effective datum by questionary. Some variables possible to affect the running result are added in this work, namely: sex, age, the year of run, height, weight, the race number of marathon, the quantity and the frequency of practices each week. Next, we use multiple regression and sliced inverse regression to increase the accuracy of the running time prediction. The best model, found here has eighty percentage's player with predictive error within fifteen minuates, which is better than the original model by Riegel(1977) with only having sixty-two percentages.

Thesis (Ph.D.)--Baylor University, 2007. / Includes bibliographical references (p. 96-98).

Thesis (M.S. in Operations Research)--Naval Postgraduate School, Sept. 2004. / Thesis Advisor(s): Samuel E. Buttrey. Includes bibliographical references (p. 73-74). Also available online.

Minimax robust designs are studied for regression models with possible misspecified response functions. These designs, minimizing the maximum of the mean squared error matrix, can control the bias caused by model misspecification and the desired efficiency through one parameter. Using nonsmooth optimization technique, we derive the minimax designs analytically for misspecified regression models. This extends the results in Heo, Schmuland and Wiens (2001). Several examples are discussed for approximately polynomial regression. / Graduate

Modelos para dados binários são bastante utilizados em várias situações práticas. Transformações em Análise de Regressão podem ser aplicadas para linearizar ou simplificar o modelo e também para corrigir desvios de suposições. Neste trabalho, descrevemos o uso de transformações nos modelos de regressão logística para dados binários e apresentamos modelos envolvendo parâmetros adicionais de modo a obter um ajuste mais adequado. Posteriormente, analisamos o custo da estimação quando são adicionados parâmetros aos modelos e apresentamos os testes de hipóteses relativos aos parâmetros do modelo de regressão logística de Box-Cox. Finalizando, apresentamos alguns métodos de diagnóstico para avaliar a influência das observações nas estimativas dos parâmetros de transformação da covariável, com aplicação a um conjunto de dados reais. / Binary data models have a lot of utilities in many practical situations. In Regrssion Analisys, transformations can be applied to linearize or simplify the model and correct deviations of the suppositions. In this dissertation, we show the use of the transformations in logistic models to binary data models and models involving additional parameters to obtain more appropriate fits. We also present the cost of the estimation when parameters are added to models, hypothesis tests of the parameters in the Box-Cox logistic regression model and finally, diagnostics methods to evaluate the influence of the observations in the estimation of the transformation covariate parameters with their applications to a real data set.

Traffic from major hurricane evacuations is known to cause severe gridlocks on evacuation routes. Better prediction of the expected amount of evacuation traffic is needed to improve the decision-making process for the required evacuation routes and possible deployment of special traffic operations, such as contraflow. The objective of this dissertation is to develop prediction models to predict the number of daily trips and the evacuation distance during a hurricane evacuation. Two data sets from the surveys of the evacuees from Hurricanes Katrina and Ivan were used in the models' development. The data sets included detailed information on the evacuees, including their evacuation days, evacuation distance, distance to the hurricane location, and their associated socioeconomic characteristics, including gender, age, race, household size, rental status, income, and education level. Three prediction models were developed. The evacuation trip and rate models were developed using logistic regression. Together, they were used to predict the number of daily trips generated before hurricane landfall. These daily predictions allowed for more detailed planning over the traditional models, which predicted the total number of trips generated from an entire evacuation. A third model developed attempted to predict the evacuation distance using Geographically Weighted Regression (GWR), which was able to account for the spatial variations found among the different evacuation areas, in terms of impacts from the model predictors. All three models were developed using the survey data set from Hurricane Katrina and then evaluated using the survey data set from Hurricane Ivan. All of the models developed provided logical results. The logistic models showed that larger households with people under age six were more likely to evacuate than smaller households. The GWR-based evacuation distance model showed that the household with children under age six, income, and proximity of household to hurricane path, all had an impact on the evacuation distances. While the models were found to provide logical results, it was recognized that they were calibrated and evaluated with relatively limited survey data. The models can be refined with additional data from future hurricane surveys, including additional variables, such as the time of day of the evacuation.

This thesis deals with regression analysis of experimentally measured data of processed cheese. There is a polynomial regression used. The choice of regressors is based on Stepwise Regression and Mallows's Statistics. The estimation of the mean value is used to find the best mixture of the emulsifying salts with regards to the observed characteristic of the processed cheese. Analysis of the experiment and its results are well documented graphically.

The mitigation of outliers serves to increase the strength of a relationship between variables. This study defined outliers in three different ways and used five regression procedures to describe the effects of outliers on 50 data sets. This study also examined the relationship among the shape of the distribution, skewness, and outliers.