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 46 for “"heteroscedastic"”.
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Estimation and inference for conditionally heteroscedastic models
… Gaussian distributions, but troublesome with heteroscedastic or non-Gaussian errors. For the latter nonstandard case, we use the weighted quantile regression (l$\sb1$) method, gaining both robustness and efficiency, with successful applications to interval forecasting of ARCH type time series …
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Contributions to Conditional Heteroscedastic Models: M-Estimation and Other Methods.
This research makes contributions to conditional heteroscedastic models in financial time series. A class of M-estimators for time series models with asymmetric form of heteroscedasticity are developed. A weighted resampling method is used to approximate the sampling distribution of M-estimators. …
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Random forests and their application to heteroscedastic drug design data
… the uncertainty information that arises in heteroscedastic data – datasets where the amount of noise in the target value varies between datapoints. We consider datasets where the relative amount of measurement noise in different datapoints is known. Using 10 drug design datasets we show …
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Consistency and Uniform Bounds for Heteroscedastic Simulation Metamodeling and Their Applications
Heteroscedastic metamodeling has gained popularity as an effective tool for analyzing and optimizing complex stochastic systems. A heteroscedastic metamodel provides an accurate approximation of the input-output relationship implied by a stochastic simulation experiment whose output is subject to …
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Essays on Conditional Heteroscedastic Time Series Models with Asymmetry, Long memory, and Structural Changes
<p>"The volatility of asset returns is usually time-varying, necessitating the introduction of models with a conditional heteroskedastic variance structure. In this dissertation, several existing formulations, motivated by the Generalized Autoregressive Conditional Heteroskedastic (GARCH) type …
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An evaluation of univariate time-series models of quarterly earnings per share and their generalization to models with autoregressive conditionally heteroscedastic disturbances
This study evaluates time-series models of quarterly earnings per share (EPS) in order to determine whether there are any changes in the residual variance to be modeled by the GARCH procedure. The results of statistical analyses indicate the presence of GARCH effect in the residuals generated from …
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Pretest Estimators for the Two Sample Linear Statistical Model
… the covariance matrix had the simplist type of heteroscedasticity. Frequently however for two samples of economic data both the location and scale parameters differ. The two sample heteroscedastic model has received considerable attention recently with the development of a two stage test: one …
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Examining the relationship between foreign investment flows and foreign exchange reserves in Namibia
… the data is homoscedastic as desired and not heteroscedastic. Heteroscedastic is when the error term does not have constant variance. White’s (1980) test is a test of the null hypothesis of no heteroscedacity against heteroscedacity of unknown, general form. White’s (1980) test found the …
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A Dual Metamodeling Perspective for Design and Analysis of Stochastic Simulation Experiments
… taking into full account the uncertainty in the heteroscedastic variance. Lastly, we generalize VBGP for handling large-scale heteroscedastic datasets based on the idea of ``transductive combination of GP experts.''
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Inference of time series regression models with weakly dependent errors
… macroeconomic series are often observed to have heteroscedastic behavior. In Chapter 3, we introduce short-memory nonstationary error framework that can accommodate a wide range of nonstationary linear processes or modulated stationary processes in the context of trend assessment setting. We …
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On semiparametric regression and data mining
… selection, missing value analysis, outliers and heteroscedastic noise. We focus on function estimation using penalised splines via mixed model methodology (Wahba 1990; Speed 1991; Ruppert et al. 2003). In dealing with the difficulties associated with data mining applications many of the models we …
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Separation of precious metal beta from a JSE multivariate model with macroeconomic variables
… in the form of Autoregressive Conditional Heteroscedastic (ARCH) and Generalized Autoregressive Conditional Heteroscedastic (GARCH) models was used and applied to test and create the APT model to address the objective. The other purpose of this dissertation was to separate beta attributable …
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Precision of the path of steepest ascent in response surface methodology
… to cover situations with correlated and heteroscedastic responses, including the important class of generalised linear models. It is shown how the eigenvalues of a certain matrix can be used to express the proportion of included directions in the confidence cone for the path of steepest …
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LINEAR HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL DATA UNDER HETEROSCEDASTICITY
… the two-sample Behrens-Fisher problem, the heteroscedastic one-way MANOVA, and the general linear hypothesis under heteroscedasticity. Although these problems have been thoroughly studied in the classical setting, high-dimensional data make most classical methods invalid due to the …
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Variational Approximation for Complex Regression Models
… framework. Models considered include mixtures of heteroscedastic regression models, mixtures of linear mixed models and generalized linear mixed models. The advantages of variational methods as compared to MCMC methods are illustrated in model fitting, model selection and model criticism. In …
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Quantile regression and survival analysis
… variables under homoscedastic linear models and heteroscedastic linear models is proposed. Comparison of the direct method with the studentization and the bootstrap methods are discussed in terms of computation and asymptotic theory. Simulation results show that the direct method has the …
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Semiparametric Inference
… varying dispersion. The algorithm is derived for heteroscedastic error generalized linear models, but it can be extended to more general setting for example censored data. Thirdly, we develop a multivariate intersection-union bioequivalence test. The intersection- union test is uniform more …
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Identification of Data Structure with Machine Learning: From Fisher to Bayesian networks
… which is a desired feature when dealing with heteroscedastic data. To address the third aspect, the constraint-based PC-algorithm is the starting point of many structure learning algorithms, it is focused on finding feature associations by means of conditional independent tests. This is then …
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Fisher Information Test of Normality
… in dealing with residuals being possibly heteroscedastic and correlated suffer from a loss of power. The approach with the fewest undesirable features is to use the Ordinary Least Squares (OLS) residuals in place of independent observations. From simulations, it is shown that one has to be …
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