Thesis (doctoral)--Universität Tübingen.

Thesis (doctoral)--Universität Rostock.

Prompts are implemented often in training procedures, to include conditional discriminations, and this can lead to prompt dependency. The current study extends a prior study that suggested that the effectiveness of supplementary visual stimuli displaying the sample and comparison was dependent on the timing in relation to the selection task, presented as a prompt or feedback, in a match-to-sample procedure. The current research examines if the differences in that effectiveness were due to differences in observing behavior in those two conditions. Measures of observing behavior were determined by making access to the individual visual stimuli contingent on clicking on the visual stimulus and keeping the cursor located on the stimulus. Participants viewed the sample comparison much less than the comparison stimulus in both the prompt and feedback conditions. Latency to select the comparison stimulus was much shorter for the prompt condition suggesting that the participants might have interacted differently with the selection task in the two conditions.

MASTER THESIS MODELLING AND COMPARATIVE ANALYZES OF VOLATILITY SPILLOVER BETWEEN US, CZECH REPUBLIC AND SERBIAN STOCK MARKETS Abstract This paper estimates Serbian, Czech and US stock markets volatility. Few studies analyzed stock market linkages for these three markets. The mean equation is estimated using the vector auto- regression model. The second moments is further estimated using different multivariate GARCH models. We find that current conditional volatilities for each stock is highly affected by the past innovations. Cross-market correlations are significant as well. However, there is a higher conditional correlation between Czech and US stock market indices compared to the conditional correlation between Serbian and US stock indices.

This study used mediation analysis to evaluate Beck’s model and Abramson’s model in a sample of 198 girls, in the 4th through 7th grades. Data from diagnostic interviews were used to create a continuous measure of depressive symptoms. Self-report measures, including the Life Events Checklist, the Cognitive Triad Inventory for Children, and the Children’s Cognitive Style Questionnaire, were used to assess perceptions of negative life events, Beck’s cognitive triad, and Abramson’s cognitive inferential style, respectively. Results of separate mediation analyses supported both Beck’s and Abramson’s cognitive theories of depression in children. There was a significant indirect effect of life events through Beck’s cognitive triad on symptoms of depression. Similarly, there was a significant indirect effect of life events through Abramson’s negative cognitive inferential style on depressive symptoms. Higher depressive symptoms were associated with a more depressotypic cognitive triad, a more depressotypic cognitive inferential style, and a greater number and greater magnitude of negative life events. Developmentally-sensitive analyses using the weakest link approach and moderated mediation (conditional process) analysis found support for both Beck’s and Abramson’s models. However, there was also some support for the developmental hypothesis that in younger children negative life events can have a direct effect on symptoms of depression; for the 4th graders in this study, direct effects and indirect effects were significant. However, for the 5th, 6th, and 7th graders indirect effects were significant, but direct effects ceased to be significant. Implications and suggestions for future research are provided. / text

Abstract: In this thesis, we give a general construction of a conditional model through embedding that concept into the concept of unconditional model. Formally, the conditional model is considered as a statistical model bearing on all the variables, i.e. on the "endogenous variables" Y and the conditioning, or "exogenous", variables Z such that j, the parameter characterizing the marginal distribution of Z, is a nuisance parameter that is identified and "well-separated” from q, the parameter of interest characterizing the Z-conditional distribution. Therefore, a family of marginal distributions on the exogenous variables and a family of “well specified” transitions of probabilities, playing a role of conditional probabilities in a global model, characterize a conditional model. Typically, but not always, j takes values in a "thick" subset F, of all the probability distributions of Z. From this construction, we analyze the identification of a conditional model in the framework of the identification of a function of the parameters in unconditional model. We propose a definition of identification in conditional models called weak identification, derived from the usual concept of identification in unconditional models. We show, under a separability condition, that weak identification may be considered as a generalization of definitions usually met in the statistical literature; in particular those in Manski (1988) and Matzkin (1993). However, an undesirable property of weak identification is shown, namely that under rather general conditions, the weak identification does not depend on the sample size. As an alternative, three other levels of identification are given, stressing the proper role of the randomness of the conditioning variables. Similar distinctions are also shown to be relevant for properties of estimators, such as unbiasedness or consistency. The relationships between these different levels of identification, unbiasedness and consistency are given. Another aspect analyzed in this thesis is the concept of partial sufficiency. Our contribution to this area is to give some further properties of S-sufficiency. In particular, we establish the connection between S-sufficiency and the identification concept for unconditional models and also for conditional models with partially observable endogenous variables. We show that when we reduce the structural (latent) model by marginalizing w.r.t an S-sufficient statistic, we do not lose the identification of the parameter of interest in the statistical (reduced) model. Furthermore, we study the properties and the conditions of applicability of S-sufficiency, with a view to compare the properties of the standard concept of sufficiency and of S-sufficiency respectively. As an application, we analyze the identification of the conditional binary response models from the semi-parametric point of view.

Thesis (M.A.)--Trinity International University, 2000. / Abstract. Includes bibliographical references (leaves 141-147).

Optimal strategies under worst-case scenarios have been studied in Bernard et al. [2013a]. Bernard et al. utilize copulas to construct cost-efficient strategies with a predefined dependence structure in the tail between the payoff and the market. In their study they show that such strategies with state-dependent copula constraints dominate traditional diversification strategies in terms of the provided protection in the states of market downturns. We derive similar strategies, however using correlation constraints instead of copula constraints in the tail. We found that for an investor seeking negative dependence with the market, it is cheaper to construct a strategy with conditional correlation constraint in the tail. However, the constructed strategies with conditional correlation constraints do not provide sufficient protection in bad states of the economy. Therefore, when analyzing a strategy, negative correlation with the market in the tail is not a sufficient indicator for the protection level in the event of a market crisis.

Thesis (doctoral)--Universität Marburg.

Inaugural-Diss. / Berlin, Freie-Universität ; 1993. / Includes bibliographical references (p. iii-xi).