Model selection criteria frequently arise from constructing estimators of discrepancy measures used to assess the disparity between the data generating model and a fitted approximating model. The widely known Akaike information criterion (AIC) results from utilizing Kullback's directed divergence (KDD) as the targeted discrepancy. Under appropriate conditions, AIC serves as an asymptotically unbiased estimator of KDD. The directed divergence is an asymmetric measure of separation between two statistical models, meaning that an alternate directed divergence may be obtained by reversing the roles of the two models in the definition of the measure. The sum of the two directed divergences is Kullback's symmetric divergence (KSD). A comparison of the two directed divergences indicates an important distinction between the measures. When used to evaluate fitted approximating models that are improperly specified, the directed divergence which serves as the basis for AIC is more sensitive towards detecting overfitted models, whereas its counterpart is more sensitive towards detecting underfitted models. Since KSD combines the information in both measures, it functions as a gauge of model disparity which is arguably more balanced than either of its individual components. With this motivation, we propose three estimators of KSD for use as model selection criteria in the setting of generalized linear models: KICo, KICu, and QKIC. These statistics function as asymptotically unbiased estimators of KSD under different assumptions and frameworks. As with AIC, KICo and KICu are both justified for large-sample maximum likelihood settings; however, asymptotic unbiasedness holds under more general assumptions for KICo and KICu than for AIC. KICo serves as an asymptotically unbiased estimator of KSD in settings where the distribution of the response is misspecified. The asymptotic unbiasedness of KICu holds when the candidate model set includes underfitted models. QKIC is a modification of KICo. In the development of QKIC, the likelihood is replaced by the quasi-likelihood. QKIC can be used as a model selection tool when generalized estimating equations, a quasi-likelihood-based method, are used for parameter estimation. We examine the performance of KICo, KICu, and QKIC relative to other relevant criteria in simulation experiments. We also apply QKIC in a model selection problem for a randomized clinical trial investigating the effect of antidepressants on the temporal course of disability after stroke.

This thesis is to study the relationship among transformational leadership, organizational commitment and organizational citizenship behavior within team levels and cross-levels. The analysis demonstrated in this thesis is based on 305 questionnaires collected from 63 leaders and 242 questionnaires from team members. The conclusions are listed as following. 1. The relationship between transformational leadership and organizational commitment (1) Transformational leadership positively impacts organizational commitment. (2) If the team members feel the inspiration from leaders, it will positively impact team members¡¦ value commitment; if they feel leaders¡¦ Idealized Influence, they will be positively impacted in retention commitment. (3) Transformational leadership is not the key factor of influencing team members¡¦ organizational commitment. 2. The relationship between transformational leadership and organizational citizenship behavior (1) If the leaders enhance their transformational leadership, it will be helpful of strengthening team members¡¦ OCB in the aspects of identification with the company, interpersonal harmony, civic virtue, conscientiousness and altruism. (2) In cross level, transformational leadership does effect the correlation to interpersonal harmony.

Both advertising and research and development (R&D) can be viewed as two factors crucial to long-term corporate growth. The purpose of this study is to investigate the effects of the advertising, R&D and interaction between advertising and R&D on shareholder value concerning economic scale and industry concentration. The empirical results show R&D investments may generate innovative products which enhance shareholder value. Moreover, the interaction between advertising and R&D is significantly and positively related to shareholder value. In practice, advertising plays a role to build brand awareness for innovative products. Additionally, we examine how economic scale and industry concentration influence the effects of advertising and R&D on shareholder value individually. With the respect to economic scale, advertising and R&D strategies may increase shareholder value more significantly for firms with high economic scale (large firms). The synergy between advertising and R&D is only significant and positive for firms with low economic scale (small firms). This implies that small firms should invest in advertising to build brand awareness and promote new products while large firms have already developed brand awareness, so the large firms should specialize in core competences. Firms in competitive industry rely more on successful advertising campaigns to increase sales. Moreover, economic scale and industry concentration significantly moderate the effectiveness of advertising and R&D. Under the limited firm sources, managers should decide the appropriate mix of advertising and R&D to maximize shareholder value significantly according to economic scale and industry concentration.

The purpose of this study is to discuss the relationship among company characteristics, brand traits and organizational attractiveness. Most of previous studies about organizational attractiveness are mainly focus on job information, industry and organization performance. Therefore, this study is seeking to understand the influence of company characteristics and brand traits to organizational attractiveness during the job seeking period. This study selects 30 Taiwanese local companies with stock release from the research of Cheers Magazine ¡u2011 The most attractive company for the new generation- Top 100 ¡vand 460 MBA students as questionnaires. By adapting the hierarchical linear model to analyze the data and obtain the result. The study found out that company characteristics and brand traits both have positive influence on organizational attractiveness. Besides, there are also influence between the company characteristics and brand traits.

Semiparametric models play important roles in the field of biological statistics. In this dissertation, two types of semiparametic models are to be studied. One is the partially linear model, where the parametric part is a linear function. We are to investigate the two common estimation methods for the partially linear models when the data is correlated — longitudinal or clustered. The other is a semiparametric model where a latent covariate is incorporated in a mixed effects model. We will propose a semiparametric approach for estimation of this model and apply it to the study on colon carcinogenesis. First, we study the profilekernel and backfitting methods in partially linear models for clustered/longitudinal data. For independent data, despite the potential rootn inconsistency of the backfitting estimator noted by Rice (1986), the two estimators have the same asymptotic variance matrix as shown by Opsomer and Ruppert (1999). In this work, theoretical comparisons of the two estimators for multivariate responses are investigated. We show that, for correlated data, backfitting often produces a larger asymptotic variance than the profilekernel method; that is, in addition to its bias problem, the backfitting estimator does not have the same asymptotic efficiency as the profilekernel estimator when data is correlated. Consequently, the common practice of using the backfitting method to compute profilekernel estimates is no longer advised. We illustrate this in detail by following Zeger and Diggle (1994), Lin and Carroll (2001) with a working independence covariance structure for nonparametric estimation and a correlated covariance structure for parametric estimation. Numerical performance of the two estimators is investigated through a simulation study. Their application to an ophthalmology dataset is also described. Next, we study a mixed effects model where the main response and covariate variables are linked through the positions where they are measured. But for technical reasons, they are not measured at the same positions. We propose a semiparametric approach for this misaligned measurements problem and derive the asymptotic properties of the semiparametric estimators under reasonable conditions. An application of the semiparametric method to a colon carcinogenesis study is provided. We find that, as compared with the corn oil supplemented diet, fish oil supplemented diet tends to inhibit the increment of bcl2 (oncogene) gene expression in rats when the amount of DNA damage increases, and thus promotes apoptosis.

This work is inspired by Lipsitz (1998) and Allport (1954) because both authors connect micro level processes to social macro level patterns. Allport’s Nature of Prejudice sought to understand patterns of anti-Semitism as connected to a larger social context. From this work, Allport developed the contact hypothesis which is premised on the idea that diversity helps alleviate racial tensions. Lipsitz’ Possessive Investment in Whiteness connects White racial privilege to a history of racial social inequality. In conintuum, I develop the nuances on prejudice formation as it leads to the denial of racial privilege or to the conflation of privileges as oppression. While I focus on White racial privilege, the theoretical contribution of my research develops the framework for individual privilege formation. I then draw upon Bonilla-Silva’s (2013) racial colorblind theory to emphasize the connection between privilege and larger patterns of racial attitudes. The macro level contribution of this dissertation focuses on patterns of overt and colorblind attitudes as affected by racial segregation, social inequality, and respondent characteristics. Data was gathered from the 2000 General Social Survey, 2010 GSS, and U.S. Census county data and applied to a hierarchical linear model. Due to sample selection, this research focuses on racial Whites’ attitudes about the racial Black population. I use measures of racial segregation as proxies for racial contact. I find patterns of racial tolerance through a ‘separate but equal’ storyline among White-Black segregation. When using, social demographics with all minorities included, I find that Whites’ attitudes about racial Blacks are attenuated. This finding supports the literature that non-Black racial minorities act as buffers for White-Black racial relations. Racial diversity is one element in helping alleviate negative racial sentiments, but patterns of segregation and social inequality impact the benefits of this racial diversity.

Formal long-term athlete development programmes emerged at the turn of the century and, despite some fierce criticisms, have evolved significantly since their inception. The first generation of athletes to grow up with these systems are now coming of age. The purpose of this thesis was to track a population of adolescent school-level swimmers between the ages of 12 and 18 years over an 8-year period so as to assess their performance progression as they matured under these athlete development programmes. The first study aimed to track the performances of the sub-elite athletes at an annual international school championship and to compare their progression with those of both junior elite and elite-level swimmers. In addition to narrowing the gender gap, the records of the sub-elite swimmers have continued to improve. In contrast, both of these factors remained relatively stable for junior elite and elite-level swimmers over the same period. Swimming affords athletes the possibility of within-sport specialisation. This almost unique aspect of swimming led to the two investigations of the second study. Firstly, the paired stroke combinations preferred by swimmers were determined using Cohen’s Kappa tests in a cross-sectional design. Secondly, the stability in the event selection of each swimmer during their adolescent years was explored longitudinally. Both males (33.9±5.8%) and females (36.9±6.5%) preferred to swim the 50 and 100 m freestyle events together over any other paired stroke combination. The majority of swimmers preferred to specialise in specific stroke techniques over distance specialisms with breaststroke being the only stroke in which swimmers of both sexes chose to specialise early. Most notable was that females specialised earlier than males. Studies three (males, n = 446) and four (females, n = 514) utilised mixed linear modelling to determine the quadratic functions of the performance progressions of adolescent swimmers (between the ages of 12 and 19 y) in seven individual competition events. Males progressed at more than twice the rate of females (3.5 and 1.7% per year, respectively) in all strokes over this age range. This was likely due to the fact that females reach puberty before males. Thresholds of peak performance occurred between the ages of 18.5±0.1 y (50 m freestyle and the 200 m individual medley) and 19.8±0.1 y (100 m butterfly) for males, but between the wider range of 16.8±0.2 y (200 m individual medley) and 20.6±0.1 y (100 m butterfly) for females. Using an independent sample of Dutch Junior national swimmers (n = 13), the fifth and final study aimed to evaluate the efficacy of the models developed in studies three and four as both target setting and talent identification tools. This was achieved through a mixed-methods approach where quantitative and qualitative data confirmed the applicability of the models for adolescent swimmers of any skill level. This thesis demonstrates that sub-elite swimmers have probably benefitted from first generation athlete development models. Longitudinal modelling of their data provides a valuable platform from which all adolescent swimmers can be compared and used to inform the next generation of bespoke swimming-specific youth development programmes.

Abstract In time delay estimation the correlator or, equivalently, matched filter estimator is widely used. Examples of its usage can be found in the global positioning system (GPS), radars and code division multiple access (CDMA) communication systems. Although widely used its performance is not studied in general case until recently. Partially this study is done in this thesis. If interfering signals like multipath or multiple access signals exist in addition to additive white Gaussian noise, as in GPS and CDMA, the correlator is not a maximum likelihood (ML) estimator. However, it is known that the correlator produces consistent estimates in the existence of multipath interference if the delay separation is larger than the correlation time of the signal (in direct sequence spread spectrum applications such as GPS and CDMA, the correlation time approximately equals the chip duration of the spreading code). It also performs well in the existence of multiple access interference (MAI), if the powers of the MAI signals are equal to the power of the desired signal. In this thesis the asymptotic distribution of the correlator estimator is derived in multisignal environments. Using the result, it can be analytically shown, that in these benign interference cases the exact ML estimator and the correlator estimators perform equally well in the sense that their asymptotic covariance matrices are equal. The thesis also verifies the well known result that if the signals are orthogonal, then the correlator and ML estimators perform equally. In addition, the correlator's asymptotic performance is investigated also in the inconsistent case by slightly extending the earlier results found in the literature. Also the resolution of the correlator estimator is investigated. It is numerically shown that the correlator estimator can produce consistent estimators even if the delay separation is less that the chip duration, which is commonly believed to be the resolution limit of the correlator. This can happen in fading channels where the multipath amplitudes are uncorrelated or just slightly correlated. This result seems to be fairly unknown. In addition to the classical ML estimator, where all the unknowns are assumed to be deterministic, also an improved ML estimator is investigated. This other ML estimator is obtained by assuming that the amplitudes are Gaussian distributed. It is an improved estimator in the sense that its asymptotic covariance, say CML, is less positive definite than that of the classical ML estimator CCML, i.e., CCML-CML is positive semidefinite. More importantly, this result is valid independent of the fact are the amplitudes really deterministic or Gaussian. This well known result is shown in this thesis to be valid also if the signals contain more than one unknown parameter, which occurs, for example, in direction-of-arrival estimation when two angles per arrival are to be estimated.

Forecasting Election Results in the Czech Republic Kateřina Doskočilová In this thesis, a forecasting model for the 2017 legislative election in the Czech Republic is built. As the Czech Republic has a multi-party system, the outcomes of the model are the expected vote shares for each party. There are two types of forecasts calculated. Firstly, a poll-based forecast using a dynamic linear model and Kalman filter to weigh the information in the polls. Secondly, the prices on betting markets are translated into probabilistic forecasts for the expected vote shares. This is a novel approach as prediction markets were previously used to forecasts only the probabilities of winning an election. Finally, the two types of forecasts are combined into one and weighed by their variance. Comparing the forecasts, we conclude that the betting market is able to predict the exact vote shares the most accurately right before the election.

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