Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (p. [77]-80).

Diss. Stockholm : Stockholms universitet, 2008.

abstract: Schennach (2007) has shown that the Empirical Likelihood (EL) estimator may not be asymptotically normal when a misspecified model is estimated. This problem occurs because the empirical probabilities of individual observations are restricted to be positive. I find that even the EL estimator computed without the restriction can fail to be asymptotically normal for misspecified models if the sample moments weighted by unrestricted empirical probabilities do not have finite population moments. As a remedy for this problem, I propose a group of alternative estimators which I refer to as modified EL (MEL) estimators. For correctly specified models, these estimators have the same higher order asymptotic properties as the EL estimator. The MEL estimators are obtained by the Generalized Method of Moments (GMM) applied to an exactly identified model. The simulation results provide promising evidence for these estimators. In the second chapter, I introduce an alternative group of estimators to the Generalized Empirical Likelihood (GEL) family. The new group is constructed by employing demeaned moment functions in the objective function while using the original moment functions in the constraints. This designation modifies the higher-order properties of estimators. I refer to these new estimators as Demeaned Generalized Empirical Likelihood (DGEL) estimators. Although Newey and Smith (2004) show that the EL estimator in the GEL family has fewer sources of bias and is higher-order efficient after bias-correction, the demeaned exponential tilting (DET) estimator in the DGEL group has those superior properties. In addition, if data are symmetrically distributed, every estimator in the DGEL family shares the same higher-order properties as the best member.   / Dissertation/Thesis / Ph.D. Economics 2013

In recent years, the development of follow-on biological products (biosimilars) has received increasing attention. The dissertation covers statistical methods related to three topics of Non-inferiority (NI), Bioequivalence (BE) and Equivalence in demonstrating biosimilarity. For NI, one of the key requirements is constancy assumption, that is, the effect of reference treatment is the same in current NI trials as in historical superiority trials. However if a covariate interacts with the treatment arms, then changes in distribution of this covariate will result in violation of constancy assumption. We propose a modified covariate-adjustment fixed margin method, and recommend it based on its performance characteristics in comparison with other methods. Topic two is related to BE inference for log-normal distributed data. Two drugs are bioequivalent if the difference of a pharmacokinetics (PK) parameter of two products falls within prespecified margins. In the presence of unspecified variances, existing methods like two one-sided tests and Bayesian analysis in BE setting limit our knowledge on the extent that inference of BE is affected by the variability of the PK parameter. We propose a likelihood approach that retains the unspecified variances in the model and partitions the entire likelihood function into two components: F-statistic function for variances and t-statistic function for difference of PK parameter. The advantage of the proposed method over existing methods is it helps identify range of variances where BE is more likely to be achieved. In the third topic, we extend the proposed likelihood method for Equivalence inference, where data is often normal distributed. In this part, we demonstrate an additional advantage of the proposed method over current analysis methods such as likelihood ratio test and Bayesian analysis in Equivalence setting. The proposed likelihood method produces results that are same or comparable to current analysis methods in general case when model parameters are independent. However it yields better results in special cases when model parameters are dependent, for example the ratio of variances is directly proportional to the ratio of means. Our research results suggest the proposed likelihood method serves a better alternative than the current analysis methods to address BE/Equivalence inference.

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Origin-Destination (O-D) matrices are required in order to model traffic routing behavior in networks. Direct techniques for collecting O-D information from home and roadside interviews have historically been utilized to estimate O-D tables. However, these techniques are not only very costly, labor intensive, and disruptive to trip makers, but traditionally also do not capture traffic peaking behavior, which is often required for traffic operational purposes. Consequently, more cost-effective indirect or synthetic O-D estimation techniques have been developed, and continue to be developed. They utilize readily available traffic volume counts to estimate the most likely O-D tables that may have generated the observed link counts. This thesis describes the basic formulations that have been proposed to formulate and solve the static O-D problem synthetically using link flow observations based on Maximum Entropy techniques. As is the case with many mathematical solutions to engineering problems, a number of simplifying assumptions have been made in order to solve the synthetic O-D problem. Unfortunately, the descriptions of these simplifying assumptions are often not fully described in the literature, and in some cases, these assumptions are not mentioned at all. Furthermore, the literature fails to systematically demonstrate what impact these assumptions have on the final O-D table estimate. Therefore, this thesis utilizes simple hypothetical networks to; 1. Demonstrate and compare the two main types of synthetic O-D formulations, namely the trip and volume based formulations. 2. Track the O-D estimation procedure from its initial formulation to its final formulation, demonstrating all significant assumptions that have been made and the implications of these assumptions on the final solution. 3. Demonstrate to what extent the final O-D estimation formulation remains valid when these assumptions are invoked. 4. Test the applicability of some packages which implement the various formulations and solution techniques that are available. / Master of Science

Fall related injuries are a major public health concern due to their high associated medical costs and negative impact on quality of life. Obese and older adults are reported to fall more frequently than their normal-weight and young counterparts. To help identify potential mechanisms of these falls the purpose of the research within this dissertation was to investigate the effects of obesity and age on the likelihood of tripping and subsequent balance recovery. Four experimental studies were conducted. The purpose of the first study was to investigate the effects of obesity, age and gender on the likelihood of tripping during level walking. Likelihood of tripping was assessed with median minimum foot clearance (MFC) and MFC interquartile range (IQR). Obesity did not increase the likelihood of tripping suggesting the increased rate of falls among obese adults is not likely due to a greater likelihood of tripping over an unseen obstacle. Additional results suggested females and individuals of shorter stature have an increased likelihood of tripping compared to their male and taller counterparts. The purpose of the second study was two-fold. First, the effects of load carriage and ramp walking on the likelihood of tripping were investigated, followed by investigating the effects of age and obesity on the likelihood of tripping during load carriage and ramp walking. Again, likelihood of tripping was assessed with median MFC and MFC IQR. Load carriage increased the likelihood of tripping during both level and ramp walking and obesity and age increased the likelihood of tripping during selected combinations of load carriage and/or ramp walking. These results suggest that the increased rate of falls during load carriage and the increased rate of falls among obese and older adult workers reported elsewhere may be due in part to an increased likelihood of tripping. The third study proposed a new method for investigating the likelihood of tripping as a function of obstacle height. The proposed method aimed to clear up ambiguous results often encountered when using MFC central tendency and variability to quantify likelihood of tripping. The method used trip probability curves and a statistical bootstrapping technique to compare trip probability at specific obstacle heights between groups of interest. An additional benefit of this method was that it was able to identify effects of factors not identifiable by the commonly used ANOVA analysis using MFC central tendency and variability. The purpose of the fourth study was to investigate the effects of obesity, age and gender on balance recovery following a lab induced trip perturbation. Measures of balance recovery included fall rate, stepping strategy and characteristics, and trunk kinematics. Obese, older, and female adults fell more frequently after tripping and this higher fall rate may help explain the higher fall rates among obese, older and female adults reported elsewhere. Failed recoveries were associated with higher peak trunk angles and angular velocities in addition to the use of a lowering strategy. Obese, older, and female adults had higher peak trunk angles and angular velocities and older adults and females used lowering strategies more often. These alterations in trunk kinematics and stepping strategy may have contributed to the higher fall rate among these individuals. / Ph. D.