In this paper, a rheumatoid arthritis (RA) medicine clinical dataset with an ordinal response is selected to study this new medicine. In the dataset, there are four features, sex, age,treatment, and preliminary. Sex is a binary categorical variable with 1 indicates male, and 0 indicates female. Age is the numerical age of the patients. And treatment is a binary categorical variable with 1 indicates has RA, and 0 indicates does not have RA. And preliminary is a five class categorical variable indicates the patient’s RA severity status before taking the medication. The response Y is 5 class ordinal variable shows the severity of patient’s RA severity after taking the medication. The primary aim of this study is to determine what factors play a significant role in determine the response after taking the medicine. First, cumulative logistic regression is applied to the dataset to examine the effect of various factors on ordinal response. Secondly, the ordinal response is categorized into two classes. Then logistic regression is conducted to the RA dataset to see if the variable selection would be different. Moreover, the shrinkage methods, elastic net and lasso are used to make a variable selection on the RA dataset of two-class response for the purpose of adding penalization to increase the model’s robustness.The four model results were compared at the end of the paper. From the comparison result, logistic regression has a better performance on variable selection than the other three approaches based on P-value.

No description available.

A whole genome scan was conducted to identify and fine map QTL regions for ultrasound and carcass merit traits in beef cattle. A total of 465 steers and bulls, genotyped for 4592 SNPs, were analysed for 16 ultrasound and carcass merit traits using interval mapping, single marker regression and Bayesian shrinkage approaches. Thirty QTL and 22 SNPs associated with traits were identified by interval mapping and single marker regression respectively. In Bayesian shrinkage estimation, 218 QTL were identified, wherein 11 of the 30 QTL identified by interval mapping were confirmed. The proportions of QTL variance on the trait variations estimated by Bayesian shrinkage analysis were relatively small. They ranged from 0.1 to 4.8% compared to 6.1 to 11.7% in interval mapping because the QTL in Bayesian approach were adjusted to remove effects of other QTL in the genome. These results are useful for detection of underlying causative QTN variants. / Animal Science

My thesis consists of three chapters. The first chapter uses the Factor-augmented Error Correction Model in model averaging for predictive regressions, which provides significant improvements with large datasets in areas where the individual methods have not. I allow the candidate models to vary by the number of dependent variable lags, the number of factors, and the number of cointegration ranks. I show that the leave-h-out cross-validation criterion is an asymptotically unbiased estimator of the optimal mean squared forecast error, using either the estimated cointegration vectors or the nonstationary regressors. Empirical results demonstrate that including cointegration relationships significantly improves long-run forecasts of a standard set of macroeconomic variables. I also estimate simulation-based prediction intervals for six real and nominal macroeconomics variables. The results are consistent with the point estimates, which further support the usefulness of cointegration in long-run forecasts.<div><br></div><div>The second chapter is a Monte Carlo study comparing the finite sample performance of six recently proposed estimation methods designed for large-dimensional regressions with endogeneity. The methods are based on combining shrinkage estimation with two-stage least squares (2SLS) or generalized method of moments(GMM), where both the number of regressors and instruments can be large. The methods are evaluated in terms of bias and mean squared error of the estimators. I consider a variety of designs with practically relevant features such as weak instruments and heteroskedasticity as well as cases where the number of observations is smaller/larger than the number of regressors/instruments. The consistency results show that the methods using GMM with shrinkage provide smaller estimation errors than the methods using 2SLS with shrinkage. Moreover, the results support the use of cross-validation to select tuning parameters if theoretically derived parameters are unavailable. Lastly, the results indicate that all instruments should correlate with at least one endogenous regressor to ensure estimation consistency.<br></div><div><br></div><div>The third chapter is coauthored with Mohitosh Kejriwal. We present new evidence on the nexus between democracy and growth employing the dynamic common correlated effects (DCCE) approach advanced by Chudik and Pesaran (2015), which is robust to both parameter heterogeneity and cross-section dependence. The DCCE results indicate a positive and statistically significant effect of democracy on economic growth, with a point estimate between approximately 1.5-2% depending on the specification. We complement our estimates with a battery of diagnostic tests for heterogeneity and cross-section dependence that corroborate the use of the DCCE approach.<br></div>