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International Telemetering Conference Proceedings / October 27-30, 1997 / Riviera Hotel and Convention Center, Las Vegas, Nevada / This paper proves theoretically that GDOP decreases as the number of satellites is increased.This paper proposes two recursive algorithms for calculating the GDOP and positioning solution.These algorithms not only can recursively calculate the GDOP and positioning solution, but also is very flexible in obtaining the best four-satellite positioning solution ,the best five-satellite positioning solution and the all visible satellite positioning solution according to given requirements. In the need of the two algorithms,this paper extends the definition of the GDOP to the case in which the number of visible satellites is less than 4.

Ways to reduce the impact of worry in generalized anxiety disorder (GAD) have received little experimental research attention. Previous research has found that those with GAD are vulnerable to negative emotionality immediately following periods of worry; emotion regulation strategies could be useful to mitigate reactivity following worry. One promising strategy is mindfulness, defined as sustained attention toward the present moment with an attitude of curiosity and acceptance. Experimental research has found that mindfulness reduces negative affect and improves emotion regulation. This strategy is likely more effective than thought suppression, a common strategy used in GAD. This online study recruited 300 individuals with analogue GAD who completed several self-report measures of worry severity, emotion dysregulation, mindfulness, and experiential avoidance and underwent experimental inductions of worry (versus no-worry control) and regulation strategy (mindfulness versus thought suppression versus no-strategy control) before watching a sad film clip and reporting state affect and emotion dysregulation. Contrary to hypotheses, the mindfulness manipulation did not have a buffering effect on the relation between worry and negative affect or emotion dysregulation. The only predicted significant finding indicated that the mindfulness manipulation had a main effect on negative affect, with visual trends indicating that this effect was driven by those who did not worry. An exploratory analysis indicated that a mindfulness manipulation increased positive affect following worry, however. Clinical implications and future directions are discussed.

This thesis demonstrates an efficient parallel method of solving the generalized eigenvalue problem, KΦ = M ΦΛ, where K is symmetric and M is symmetric positive-definite, by first converting it to a standard eigenvalue problem, solving the standard eigenvalue problem, and back-transforming the results. An abstraction for parallel dense linear algebra is introduced along with a new algorithm for forming A := U⁻ᵀ K U⁻¹ , where U is the Cholesky factor of M , that is up to twice as fast as the ScaLAPACK implementation. Additionally, large improvements over the PBLAS implementations of general matrix-matrix multiplication and triangular solves with many right-hand sides are shown. Significant performance gains are also demonstrated for Cholesky factorizations, and a case is made for using 2D-cyclic distributions with a distribution blocksize of one. / text

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In many practical situations when analyzing a dependence of one or more explanatory variables on a response variable it is essential to assume that the relationship of interest obeys certain shape constraints, such as monotonicity or monotonicity and convexity/concavity. In this thesis a new approach to shape preserving smoothing within generalized additive models has been developed. In contrast with previous quadratic programming based methods, the project develops intermediate rank penalized smoothers with shape constrained restrictions based on re-parameterized B-splines and penalties based on the P-spline ideas of Eilers and Marx (1996). Smoothing under monotonicity constraints and monotonicity together with convexity/concavity for univariate smooths; and smoothing of bivariate functions with monotonicity restrictions on both covariates and on only one of them are considered. The proposed shape constrained smoothing has been incorporated into generalized additive models with a mixture of unconstrained and shape restricted smooth terms (mono-GAM). A fitting procedure for mono-GAM is developed. Since a major challenge of any flexible regression method is its implementation in a computationally efficient and stable manner, issues such as convergence, rank deficiency of the working model matrix, initialization, and others have been thoroughly dealt with. A question about the limiting posterior distribution of the model parameters is solved, which allows us to construct Bayesian confidence intervals of the mono-GAM smooth terms by means of the delta method. The performance of these confidence intervals is examined by assessing realized coverage probabilities using simulation studies. The proposed modelling approach has been implemented in an R package monogam. The model setup is the same as in mgcv(gam) with the addition of shape constrained smooths. In order to be consistent with the unconstrained GAM, the package provides key functions similar to those associated with mgcv(gam). Performance and timing comparisons of mono-GAM with other alternative methods has been undertaken. The simulation studies show that the new method has practical advantages over the alternatives considered. Applications of mono-GAM to various data sets are presented which demonstrate its ability to model many practical situations.

Recently in the Netherlands and in Amsterdam, policymakers have started to see generalized trust as an indicator of societal cohesion, which is taken to be endangered by participating in ethnically homogenous or bonding organizations. However, there is no study that supports this negative socialization effect. Existing surveys in the Netherlands and in Amsterdam lack either appropriate data on ethnic minorities or do not allow this question to be properly addressed. They do not contain the relevant variables or do not have a multilevel structure, since the latter requires one to sample many responses from the same organization rather than collect data that is representative of individuals. This thesis addresses this gap in the literature by juxtaposing Turkish (bonding) associations with organizations whose membership consists of different ethnic groups (bridging). I surveyed 40 non-profit organizations in Amsterdam and collected responses from around 450 participants. I subsequently describe different bonding and bridging practices within and between organizations, and demonstrate that Turkish, as compared to mixed organizations, are internally focused on their own group, but externally are more involved in bridging networks. Thus contact within Turkish organizations is confined to fellow ethnics and this allows for testing the contact hypothesis. This thesis employs a multilevel model and distinguishes individual attributes from organizational factors (ethnic composition). However, the variance in generalized trust at the organizational level is only 4%, which indicates that the context of voluntary organizations has not much influence on it. Secondly, I test an interaction effect between the mixed ethnic composition of an organization and the length of participation in years in order to test for a socialization effect (the contact hypothesis). However, this interaction effect is not statistically significant. Finally, I test for another interaction effect, namely the effect of having a close tie in a mixed organization, in order to test for a sufficient but not necessary condition of the contact hypothesis, which might turn contact into attitude change. Again, this interaction is not statistically significant. Beyond bridging and bonding, there are complementary mechanisms which might have affected generalized trust. I, therefore, extend my model to include cognitive evaluations about one’s humanitarian values, negative life experiences and socio-economic factors. Three theoretical frameworks are tested: psychological; norm driven; and social success. The findings suggest that differences in generalized trust are best explained by individual processes rather than contact between ethnically diverse groups in voluntary organizations. Optimism has the strongest effect size on generalized trust. Other key factors are educational levels, and to some extent older age as well as having been widowed or lost one’s partner due to divorce. Younger people who adhere to humanitarian values are also among the high generalized trusters. The effect of education, age and the experience of divorce or separation is also found in other Dutch representative national samples and support the consensus around social success theories in explaining generalized trust.

Tight-binding potential combined with a simulated annealing method is used to study the generalized stacking fault structure and energy of gold. The potential is chosen to fit band structures and total energies from a set of first-principles calculations (Phys. Rev. B54, 4519 (1996)). It is found that the relaxed stacking fault energy (SFE) and anti-SFE are equal to 46 and 102 mJ/m², respectively, and in good agreement with the first principles calculations and experiment. In addition, the potential predicts that the c/a of hcp-like stacking fault structure in Au is slightly smaller than the ideal one. / Singapore-MIT Alliance (SMA)

published_or_final_version / Mathematics / Master / Master of Philosophy

In longitudinal and spatio-temporal data analysis, repeated measurements from a subject can be either regional- or temporal-dependent. The correct specification of the within-subject covariance matrix cultivates an efficient estimation for mean regression coefficients. In this thesis, robust estimation for the mean and covariance jointly for the regression model of longitudinal data within the framework of generalized estimating equations (GEE) is developed. The proposed approach integrates the robust method and joint mean-covariance regression modeling. Robust generalized estimating equations using bounded scores and leverage-based weights are employed for the mean and covariance to achieve robustness against outliers. The resulting estimators are shown to be consistent and asymptotically normally distributed. Robust variable selection method in a joint mean and covariance model is considered, by proposing a set of penalized robust generalized estimating equations to estimate simultaneously the mean regression coefficients, the generalized autoregressive coefficients and innovation variances introduced by the modified Cholesky decomposition. The set of estimating equations select important covariate variables in both mean and covariance models together with the estimating procedure. Under some regularity conditions, the oracle property of the proposed robust variable selection method is developed. For these two robust joint mean and covariance models, simulation studies and a hormone data set analysis are carried out to assess and illustrate the small sample performance, which show that the proposed methods perform favorably by combining the robustifying and penalized estimating techniques together in the joint mean and covariance model. Capturing dynamic change of time-varying correlation structure is both interesting and scientifically important in spatio-temporal data analysis. The time-varying empirical estimator of the spatial correlation matrix is approximated by groups of selected basis matrices which represent substructures of the correlation matrix. After projecting the correlation structure matrix onto the space spanned by basis matrices, varying-coefficient model selection and estimation for signals associated with relevant basis matrices are incorporated. The unique feature of the proposed model and estimation is that time-dependent local region signals can be detected by the proposed penalized objective function. In theory, model selection consistency on detecting local signals is provided. The proposed method is illustrated through simulation studies and a functional magnetic resonance imaging (fMRI) data set from an attention deficit hyperactivity disorder (ADHD) study. / published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy