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.
Results
Showing 1 to 20 of 23 for “"Cholesky decomposition"”.
-
Contributions to Large Covariance and Inverse Covariance Matrices Estimation
… multivariate financial data. An order-invariant Cholesky-log-GARCH model is developed for estimating the time-varying covariance matrices based on the modified Cholesky decomposition. This decomposition provides a statistically interpretable parametrization of the covariance matrix. The key idea …
-
Objective Bayesian Analysis of Kullback-Liebler Divergence of two Multivariate Normal Distributions with Common Covariance Matrix and Star-shape Gaussian Graphical Model
… by the common precision matrix. We use the Cholesky decomposition for re-parameterization of the precision matrix. The KL divergence is a true distance measurement for divergence between the two multivariate normal populations with common covariance matrix. Frequentist properties of the …
-
The Genetic Epidemiology of Purging Disorder, Anorexia Nervosa, and Obsessive Compulsive Personality Disorder
… both studies. Univariate analyses, a bivariate Cholesky decomposition, and an item-factor modeling approach were used. Results from Study 1 revealed estimates of 3.0%, 3.4%, 3.7%, and 11.5% for self-induced vomiting, laxative and diuretic abuse, and excessive exercise, respectively. Laxative …
-
Conjugate gradient density matrix search: A linear scaling alternative to diagonalization
… for use with first principles calculations. A Cholesky decomposition of the overlap matrix and its inverse, which can be formed in near linear time for sparse systems, is used to transform to and back from an orthonormal basis. Linear scaling of CPU time for the density matrix search and …
-
Random Effects Selection In Bayesian Accelerated Failure Time Model With Correlated Interval Censored Data
… time models. The proposed method relies on Cholesky decomposition on the random effects covariance matrix and the parameter expansion method for the selection of random effects. The Dirichlet prior is used to model the uncertainty in the random effects. The error distribution for the AFT …
-
Dutch Disease in Australia: A Structural VAR Model
… identified the structural shocks using short-run Cholesky decomposition by making system recursive. The result showed that the foreign demand shock caused to raise the price of the commodity, and shock to the commodity price cause the real exchange rate to appreciate and has a positive impact on …
-
Vectorization and microtasking of UTCHEM on the CRAY X-MP
… method was also compared to a vectorized Cholesky decomposition, which is a direct method. The method of relative changes was modified in two different ways to include all components for selection of time steps. Modifications to the restart option and dimensioning has increased the …
-
An investigation into direct sparse matrix solution schemes in the finite element method
… it was decided to use an alternative method. A Cholesky decomposition method with a hypermatrix data storage scheme was then investigated and developed. The equation solution method was developed using a virtual paging scheme as implemented by the DAS package, and a module of general hypermatrix …
-
Essays on applied econometrics
… published paper proposes the use of a modified Cholesky decomposition method, instead of the tapered estimator, for the inverse autocovariance matrix. The second chapter examines the causality between the odds of winning an election and vote-buying. In Taiwan in 2010, eight cities and counties …
-
Bayesian analysis of historical functional linear models with application to air pollution forecasting
… MCMC algorithms is increased by implementing a Cholesky decomposition to sample from high-dimensional Gaussian distributions and by taking advantage of the orthogonal properties of the functional principal components used to model the functional covariates. Our extensive simulation study shows …
-
An investigation into reference-day risk-free metrics in the context of modern portfolio theory on the JSE
… research is achieved through the use of the Cholesky decomposition and a nonparametric bootstrapping procedure to generate reference-day risk-free estimates for average returns, standard deviations, correlations and betas. Furthermore, this paper applies the reference-day risk-free metrics to …
-
Modeling and Simulation of Random Processes and Fields in Civil Engineering and Engineering Mechanics
… Method (SRM) is used. The SRM involves a Cholesky decomposition of the CSDM. However, it is a well known issue that as the length of the structure, and consequently the size of the vector process, increases, this Cholesky decomposition breaks down (from the numerical point of view). To …
-
Probabilistic Approximations of Matrix Decompositions for Inverse Problems
… A particular focus is approximate matrix decompositions. This thesis makes use of the Sherman-Morrison-Woodbury formula/matrix inversion lemma, Schur Complements, pseudoinverses, the eigenvalue decomposition, the singular value decomposition, the Cholesky decomposition and particularly the …
-
SYNTHESIS OF LINEAR MULTIVARIABLE FEEDBACK SYSTEMS IN INFINITE INDEX NORM
… part of Pick matrix from the regular part by a Cholesky decomposition. These results are also developed in a recursive version for repeated zeros. The zeros and interpolation condition vectors of a system can be determined numerically by an algorithm to solve eigenvalues and eigenvectors of a …
-
Non-parametric competing risks with multivariate frailty models
… frailty with competing risk model using Cholesky decomposition of the Lognormal frailty. Second, a correlated Inverse Gaussian frailty in the presence of competing risks model. Third, a non-parametric multivariate frailty with parametric competing risk model is proposed. Finally, a …
-
Statistical and Behavioral Modeling of Driver Behavior on Signalized Intersection Approaches
… are illustrated; namely Cascaded regression and Cholesky decomposition. Both procedures are demonstrated to produce replications that are consistent with the Bayesian model realizations, and capture the parameter correlations without the need to store the set of parameter realizations. The …
-
Disuguaglianze sociali nella salute, tra eterogeneità individuale e fattori sociali di rischio
… factors (C) or genetic factors (A). Then I run a Cholesky decomposition model in order to understand whether the observed covariance between education and BMI is due to common genetic/environmental factors. I find out that bivariate heritability is 0.30, meaning that around 30% of the observed …
-
Some aspects of fitting multinomial models in a GLM framework
… applies an interesting and simple form of the Cholesky decomposition to a matrix that consists of diagonal sub - matrices to find the iterative weight matrix W ij. This method requires no matrix algebra facilities as all the calculations are carried out in an array format. This makes it …
-
Time-to-Event Prediction Using Deep Learning Models: Application to GPU Failure Data
… and logical distances—and apply them using Cholesky decomposition. We describe how we assign a learnable embedding to each GPU location, incorporating these correlation matrices. The neural network outperforms both the machine learning models and the parametric model across various MSE …
-
Efficient formulation and implementation of ensemble based methods in data assimilation
… implementation of the EnKF based on a modified Cholesky decomposition for inverse covariance matrix estimation. This approach exploits the conditional independence of background errors between distant model components with regard to a predefined radius of influence. Consequently, sparse …
Page 1 of 2