Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 38 for “"Conditional mean"”.
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Statistical inference of multivariate time series and functional data using new dependence metrics
… [see Shao and Zhang (2014)] that quantifies the conditional mean dependence of two random vectors. For one part, the new approaches to dimension reduction of multivariate time series for conditional mean and conditional variance are proposed by applying new metrics, the so-called Martingale …
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Optimal and Safe Semi-supervised Estimation and Inference for High-dimensional Linear Regression
… semi-supervised estimator with estimates for the conditional mean function as pseudo-labels for unlabeled data, which attains the lower bound provided that the imputation for the conditional mean function is consistent with a proper rate. To tackle the problem that without any model assumptions …
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Generalized Markov branching models
… expressions for the extinction probability and mean extinction time are presented. The criteria for recurrence and ergodicity are also established. In addition, an explicit expression for the equilibrium distribution is presented. We then moved on to investigate the basic properties of an …
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School Quality Capitalization Into Housing Prices In Minnesota And Pennsylvania
… is constructed as the Cochrane-Orcutt tranformed conditional means of the spatially-lagged housing prices and quality measures. As school quality is observed on a much lower frequency than housing prices, it is not possible to estimate the conditional mean of school quality using non-parametric …
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Transition equity markets of Central Europe: volatility, predictability, integration
… previously documented characteristics of the unconditional distribution of stock returns normally used to justify the use of the GARCH class of the models of conditional volatility. Strong GARCH effects are apparent in all series examined. The estimates of asymmetric models of conditional …
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Sufficient Dimension Reduction with Missing Data
… the simplicity of SIR and requires the linear conditional mean assumption. The second part of the dissertation proposes two new estimating equation procedures: the complete case estimating equation approach and the inverse probability weighted estimating equation approach. The two approaches …
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Quantile Regression for Panel Data
… are not observed in the lower quantiles of the conditional educational attainment distribution. They also estimate a classical Gaussian random effects model to account for individual heterogeneity, but this approach precludes estimating effects other than the mean. To get around the problem, we …
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Methods for handling missing data in cohort studies where outcomes are truncated by death
… at each time point, otherwise known as 'partly conditional inference'. Partly conditional inference distinguishes between the reasons for missingness; failure to make this distinction will cause inference to be based not only on pre-death outcomes which exist but also on post-death outcomes …
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The Heavy Hand of Government: an Economic Study of Obesity
… effect of the marginal income tax rate on the conditional mean of BMI ranges from 0.20 to 0.36 under various specifications. The results of the mixed multilevel model indicate a strong and positive statistical association between the marginal income tax rate and BMI. The quantile regression …
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On modeling the volatility in speculative prices
… and parameter restrictions, but model the conditional mean and conditional variance jointly. The univariate modeling is illustrated using the Real Effect Exchange Rate(REER) indices of three mainstream currencies in Asia (RMB, Hong Kong Dollar and Taiwan Dollar), while the multivariate …
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Essays on average treatment effects
… treatment variables are present. When the true conditional mean function takes the form of exponential function, the Heckit-type linear method, while it can be a good approximation, is inconsistent for the true ATE since it is derived under the assumption of linear conditional mean. The …
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Model Selection, Uniform Inference and Nonparametric Regression
… consistency of the series estimator for the conditional mean function and its linear functionals. The uniformity holds both in the support of the covariates as well as the models considered. Under high-level assumptions, a non-asymptotic linearisation result delivers uniform rates of …
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Kernel Estimators in Complex Data Analysis
… to model data with complicated density curves/conditional mean curves. Even during this information age, when the datasets confronting us have become larger and more complicated, which seems to disfavor the use of kernel estimators because of the so- called “curse of dimensionality” phenomenon …
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The stochastic behavior of spot freight rates and the risk premium in bulk shipping
… a one-factor Markovian model can capture the mean reversion and level effect in the spot freight rate very well, an extension to a multi-factor non-Markovian model is required in order to account for the observed lag effects in the conditional mean and variance. New theory governing the risk …
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Essays in Economic *Growth and Asset Pricing
… and the effects of policy variables on the conditional distribution of the GDP growth rates. Our findings suggest that previous empirical growth studies relying on conditional mean estimation methods such as least squares give a misleading picture of the growth dynamics. Furthermore, it is …
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Three essays on identifying safe havens for equity investors
Chapter 2 examines conditional mean and volatility spillover between equity and gold to ascertain if gold is a safe haven for investors. This is achieved using a VAR-GARCH model allowing for simultaneous transmission of shocks between series. This also provides the time-varying inputs for portfolio …
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Selection and Assortative Mating
… separately and jointly. Selection based on the conditional mean of the merit variable given the data maximizes the expected value of the merit variable when the proportion selected is constant. This is optimal even if candidates to be evaluated and the available information are a function of …
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Computations of Turbulent Premixed Flames Using Conditional Moment Closure
… methods used for predicting pollutants is the conditional moment closure (CMC), which is suitable to predict pollutants with slow time scales. Despite the fact that CMC has been successfully applied to various non-premixed combustion systems, its application to premixed flames is not fully …
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Semiparametric Approaches for Dimension Reduction Through Gradient Descent on Manifold
… regression without loss of information of the conditional mean E[Y|X]. The method, gemDR, focuses on identifying the central mean subspace (CMS). Then gemDR is extended to CS-gemDR for central subspace (CS), through the empirical cumulative distribution function. CS-OPG, a modified outer …
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Count-Regression-Based Empirical Causal Analysis from a Potential Outcomes Perspective: Accounting for Boundedness, Discreteness, Dispersion and Unobservable Confounding
… (outcome has countable support) and dispersion (conditional variance and other higher order conditional moments of the outcome not necessarily equal to its conditional mean) of count data, and unobservable confounding. The focus here is on the case in which the causal variable is continuous. The …
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