Objective: This study examined (a) the distribution of physical symptoms in youth with specific primary anxiety disorders (i.e. separation anxiety disorder [SAD], generalized anxiety disorder [GAD], and social phobia [SP]) and (b) their response to treatment with cognitive-behavioral therapy (CBT; 14 sessions of CBT over the course of 12 weeks), medication, combination therapy (CBT + medication), or pill placebo in a sample. Method: Anxiety disordered youth (N = 488, age 7-17) who met criteria for a primary diagnosis of GAD, SAD, and/or SP as part of the Child/Adolescent Anxiety Multimodal Study (CAMS; Walkup et al. 2008) were included in this study. The sample was diverse and included children with comorbid secondary diagnoses. Results: The most common somatic complaints were headache, stomach pain or aches, feeling drowsy or too sleepy, head cold or sniffles, and sleeplessness. The distribution of these complaints did not differ across diagnostic groups. The number and severity of physical symptoms decreased over the course of treatment. Treatment condition, including placebo, was unrelated to the number and severity of physical symptoms posttreatment. Conclusions: Treatment of anxiety leads to a decrease in the number and severity of physical symptoms experienced in anxiety-disordered youth, irrespective of treatment type. / Psychology

The main research topic in this dissertation is the development of the closure method of multiple testing procedures. Considering a general procedure that allows the underlying test statistics as well as the associated parameters to be dependent, we first propose a step-down procedure controlling the FWER, which is defined as the probability of committing at least one false discovery. Holm (1979) first proposed a step-down procedure for multiple hypothesis testing with a control of the familywise error rate (FWER) under any kind of dependence. Under the normal distributional setup, Seneta and Chen (2005) sharpened the Holm procedure by taking into account the correlations between the test statistics. In this dissertation, the Seneta-Chen procedure is further modified yielding a more powerful FWER controlling procedure. We then advance our research and propose another step-down procedure to control the generalized FWER (k-FWER), which is defined as the probability of making at least k false discoveries. We compare our proposed k-FWER procedure with the Lehmann and Romano (2005) procedure. The proposed k-FWER procedure is more powerful, particularly when there is a strong dependence in the tests. When the proportion of true null hypotheses is expected to be small, the traditional tests are usually conservative by a factor associated with pi0, which is the proportion of true null hypotheses among all null hypotheses. Under independence, two procedures controlling the FWER and the k-FWER are proposed in this dissertation. Simulations are carried out to show that our procedures often provide much better FWER or k-FWER control and power than the traditional procedures. / Statistics

Cluster analysis is commonly described as the classification of unlabeled observations into groups such that they are more similar to one another than to observations in other groups. Model-based clustering assumes that the data arise from a statistical (mixture) model and typically a group of many models are fit to the data, from which the `best' model is selected by a model selection criterion (often the BIC in mixture model applications). This chosen model is then the only model that is used for making inferences on the data. Although this is common practice, proceeding in this way ignores a large component of model selection uncertainty, especially for situations where the difference between the model selection criterion for two competing models is relatively insignificant. For this reason, recent interest has been placed on selecting a subset of models that are close to the selected best model and using a weighted averaging approach to incorporate information from multiple models in this set. Model averaging is not a novel approach, yet its presence in a clustering framework is minimal. Here, we use Occam's window to select a subset of models eligible for two types of averaging techniques: averaging a posteriori probabilities, and direct averaging of model parameters. The efficacy of these model-based averaging approaches is demonstrated for a family of generalized hyperbolic mixture models using real and simulated data. / Thesis / Master of Science (MSc)

The poverty rate among veterans in US has increased over the past decade, according to the U.S. Department of Veterans Affairs (2015). Thus, it is crucial to veterans who live below the poverty level to get sufficient benefit grants. A study on prudently managing health benefit grants for veterans may be helpful for government and policy-makers making appropriate decisions and investments. The purpose of this research is to find an underlying group structure for the veterans' benefit grants dataset and then estimate veterans' benefit grants sought using incomplete data. The generalized linear mixed cluster-weighted model based on mixture models is carried out by grouping similar observations to the same cluster. Finally, the estimates of veterans' benefit grants sought will provide reference for future public policies. / Thesis / Master of Science (MSc)

Two randomized controlled trials were conducted to find out whether a new treatment for asthma has a significant effect on the patients. These were multi-center trials with a parallel design, the control arm receiving a Placebo. The data were collected over a period of about 20 days before administering the intervention and for almost 80 days after the intervention. Thus, each patient has many observations recorded, making the data longitudinal. The data are summarized using first descriptive statistics and graphical displays. Then, a continuation ratio model with a lagged covariate to account for the longitudinal aspect is used to model the data. Finally, Generalized Estimating Equations methods are used. These methods have acquired popularity in recent years to account for longitudinal correlation structures. To apply the continuation ratio, the data have to be appropriately restructured. Then, the logistic regression is used to model the symptoms. The results of this procedure show that the treatment is statistically significant. However, the goodness of fit tests show that the model is inadequate. This issue is explored in the last subsection of Chapter 3. Using Generalized Estimating Equations to analyze the number of times rescue medication was used, we concluded that there is no statistically significant difference between the Active and Control groups. However, we noticed that the use of rescue medication decreased with time from the start of treatment. / Thesis / Master of Science (MS)

No description available.

yes / Traditional rotor dynamics mainly focuses on the steady- state behavior of the rotor and shafting. However, for systems such as hydro turbine generating sets (HTGS) where the control and regulation is frequently applied, the shafting safety and stabilization in transient state is then a key factor. The shafting transient state inevitably involves multiparameter domain, multifield coupling, and coupling dynamics. In this paper, the relative value form of the Lagrange function and its equations have been established by defining the base value system of the shafting. Takingthe rotation angle and the angular speed of the shafting as a link, the shafting lateral vibration and generator equations are integrated into the framework of generalized Hamiltonian system. The generalized Hamiltonian control model is thus established. To make the model more general, additional forces of the shafting are taken as the input excitation in proposed model. The control system of the HTGS can be easily connected with the shafting model to form the whole simulation system of the HTGS. It is expected that this study will build a foundation for the coupling dynamics theory using the generalized Hamiltonian theory to investigate coupling dynamic mechanism among the shafting vibration, transient of hydro turbine generating sets, and additional forces of the shafting. / National Natural Science Foundation of China under Grant Nos. 51179079 and 50839003

Yes / A diesel engine is a typical dynamic system. In this paper, a dynamics method is proposed to establish the Hamiltonian model of the diesel engine, which solves the main difficulty of con-structing a Hamiltonian function under the multi-field coupling condition. Furthermore, the control method of Hamiltonian model structure modification is introduced to study the control of a diesel engine. By means of the principle of energy-shaping and Hamiltonian model structure modification theories, the modified energy function is constructed, which is proved to be a quasi-Lyapunov function of the closed-loop system. Finally, the control laws are derived, and the simulations are carried out. The study reveals the dynamic mechanism of diesel engine operation and control and provides a new way to research the modeling and control of a diesel engine system. / This research was funded by the National Natural Science Foundation of China, grant number 51869007.

The degree to which patients and their therapists align over time on their perceptions of therapeutic processes and intermediary outcomes has generally been regarded as an important element of effective psychotherapy; however, few studies have examined empirically the influence of such dyadic convergences on ultimate treatment outcomes. This study examined (a) whether early treatment convergences in patient-therapist alliance and session progress ratings were associated with subsequent worry and distress reduction (and final posttreatment level) in psychotherapy for generalized anxiety disorder (GAD), and (b) whether treatment type and the initial (session 1) levels of perceived alliance and session progress moderated these associations. Data derived from a clinical trial in which patients with severe GAD were randomly assigned to receive either 15 sessions of cognitive-behavioral therapy (CBT; n = 43) or 4 sessions of motivational interviewing (MI) followed by 11 integrative CBT-MI sessions (n = 42). Patients and therapists rated the alliance and session progress after each session. Patients rated their worry and distress multiple times throughout treatment. Dyadic multilevel modeling revealed, as predicted, that alliance convergence over the first half of therapy was associated with greater subsequent worry (p = .03) and distress (p = .01) reduction. Additionally, the combination of low initial patient-rated alliance and early divergence was associated with the worst trajectory for the distress outcome (p = .04). Unexpectedly, session progress divergence had a near significant association with lower posttreatment worry (p = .05) and was significantly associated with more accelerated subsequent distress reduction (p = .03). Additionally, for patients who perceived their initial progress more positively, whether dyads converged or diverged in early session progress ratings affected the trajectories (though not the ultimate amount) of distress change (p = .02). These findings suggest that divergence of early patient-therapist alliance perspectives, especially when coupled with lower initial patient-rated alliance quality, may be an important marker for patient nonresponse and therapist responsiveness (e.g., use of alliance repair strategies). The findings on patient-therapist session progress convergence are less straightforward, though several possible implications are discussed.

We study the Feasible Generalized Least-Squares (FGLS) estimation of the parameters of a linear regression model in which the errors are allowed to exhibit heteroskedasticity of unknown form and to be serially correlated. The main contribution is two fold; first we aim to demystify the reasons often advanced to use OLS instead of FGLS by showing that the latter estimate is robust, and more efficient and precise. Second, we devise consistent FGLS procedures, robust to misspecification, which achieves a lower mean squared error (MSE), often close to that of the correctly specified infeasible GLS. In the first chapter we restrict our attention to the case with independent heteroskedastic errors. We suggest a Lasso based procedure to estimate the skedastic function of the residuals. This estimate is then used to construct a FGLS estimator. Using extensive Monte Carlo simulations, we show that this Lasso-based FGLS procedure has better finite sample properties than OLS and other linear regression-based FGLS estimates. Moreover, the FGLS-Lasso estimate is robust to misspecification of both the functional form and the variables characterizing the skedastic function. The second chapter generalizes our investigation to the case with serially correlated errors. There are three main contributions; first we show that GLS is consistent requiring only pre-determined regressors, whereas OLS requires exogenous regressors to be consistent. The second contribution is to show that GLS is much more robust that OLS; even a misspecified GLS correction can achieve a lower MSE than OLS. The third contribution is to devise a FGLS procedure valid whether or not the regressors are exogenous, which achieves a MSE close to that of the correctly specified infeasible GLS. Extensive Monte Carlo experiments are conducted to assess the performance of our FGLS procedure against OLS in finite samples. FGLS achieves important reductions in MSE and variance relative to OLS. In the third chapter we consider an empirical application; we re-examine the Uncovered Interest Parity (UIP) hypothesis, which states that the expected rate of return to speculation in the forward foreign exchange market is zero. We extend the FGLS procedure to a setting in which lagged dependent variables are included as regressors. We thus provide a consistent and efficient framework to estimate the parameters of a general k-step-ahead linear forecasting equation. Finally, we apply our FGLS procedures to the analysis of the two main specifications to test the UIP.