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You searched for +publisher:"University of Florida" +contributor:("Doss, Hani"). Showing records 1 – 8 of 8 total matches.

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University of Florida

1. Steorts, Rebecca Carter. Bayes and Empirical Bayes Benchmarking for Small Area Estimation.

Degree: PhD, Statistics, 2012, University of Florida

 Small area estimation has become increasingly popular due to growing demand for such statistics. In order to produce estimates of adequate precision for these small… (more)

Subjects/Keywords: Bayes estimators; Benchmarking; Counties; Estimated taxes; Estimation methods; Estimators; Income estimates; Population estimates; State estimation; Statistical estimation; area-level  – bayes  – benchmarking  – bootstrap  – empirical-bayes  – fay-herriot  – small-area

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Steorts, R. C. (2012). Bayes and Empirical Bayes Benchmarking for Small Area Estimation. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043927

Chicago Manual of Style (16th Edition):

Steorts, Rebecca Carter. “Bayes and Empirical Bayes Benchmarking for Small Area Estimation.” 2012. Doctoral Dissertation, University of Florida. Accessed September 21, 2019. http://ufdc.ufl.edu/UFE0043927.

MLA Handbook (7th Edition):

Steorts, Rebecca Carter. “Bayes and Empirical Bayes Benchmarking for Small Area Estimation.” 2012. Web. 21 Sep 2019.

Vancouver:

Steorts RC. Bayes and Empirical Bayes Benchmarking for Small Area Estimation. [Internet] [Doctoral dissertation]. University of Florida; 2012. [cited 2019 Sep 21]. Available from: http://ufdc.ufl.edu/UFE0043927.

Council of Science Editors:

Steorts RC. Bayes and Empirical Bayes Benchmarking for Small Area Estimation. [Doctoral Dissertation]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0043927


University of Florida

2. Roman, Jorge C. Convergence Analysis of Block Gibbs Samplers for Bayesian General Linear Mixed Models.

Degree: PhD, Statistics, 2012, University of Florida

 We consider two widely applicable Bayesian versions of the general linear mixed model (GLMM). These Bayesian GLMMs are created by adopting a proper and an… (more)

Subjects/Keywords: Consistent estimators; Density; Ergodic theory; Estimation methods; Estimators; Markov chains; Simulations; Statistical discrepancies; Statistics; Sufficient conditions; chains  – ergodicity  – geometric  – gibbs  – markov  – sampler

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APA (6th Edition):

Roman, J. C. (2012). Convergence Analysis of Block Gibbs Samplers for Bayesian General Linear Mixed Models. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043966

Chicago Manual of Style (16th Edition):

Roman, Jorge C. “Convergence Analysis of Block Gibbs Samplers for Bayesian General Linear Mixed Models.” 2012. Doctoral Dissertation, University of Florida. Accessed September 21, 2019. http://ufdc.ufl.edu/UFE0043966.

MLA Handbook (7th Edition):

Roman, Jorge C. “Convergence Analysis of Block Gibbs Samplers for Bayesian General Linear Mixed Models.” 2012. Web. 21 Sep 2019.

Vancouver:

Roman JC. Convergence Analysis of Block Gibbs Samplers for Bayesian General Linear Mixed Models. [Internet] [Doctoral dissertation]. University of Florida; 2012. [cited 2019 Sep 21]. Available from: http://ufdc.ufl.edu/UFE0043966.

Council of Science Editors:

Roman JC. Convergence Analysis of Block Gibbs Samplers for Bayesian General Linear Mixed Models. [Doctoral Dissertation]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0043966


University of Florida

3. Buta, Eugenia. Computational Approaches for Empirical Bayes Methods and Bayesian Sensitivity Analysis.

Degree: PhD, Statistics, 2010, University of Florida

 Computational Approaches for Empirical Bayes Methods and Bayesian Sensitivity Analysis We consider situations in Bayesian analysis where we have a family of priors on the… (more)

Subjects/Keywords: Bayes estimators; Ergodic theory; Estimation methods; Estimators; Markov chains; Maximum likelihood estimations; Modeling; Point estimators; Skeleton; Statistics; analysis, bayes, bayesian, empirical, factor, hyperparameter, posterior, prior, selection, sensitivity, variable

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APA (6th Edition):

Buta, E. (2010). Computational Approaches for Empirical Bayes Methods and Bayesian Sensitivity Analysis. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0041145

Chicago Manual of Style (16th Edition):

Buta, Eugenia. “Computational Approaches for Empirical Bayes Methods and Bayesian Sensitivity Analysis.” 2010. Doctoral Dissertation, University of Florida. Accessed September 21, 2019. http://ufdc.ufl.edu/UFE0041145.

MLA Handbook (7th Edition):

Buta, Eugenia. “Computational Approaches for Empirical Bayes Methods and Bayesian Sensitivity Analysis.” 2010. Web. 21 Sep 2019.

Vancouver:

Buta E. Computational Approaches for Empirical Bayes Methods and Bayesian Sensitivity Analysis. [Internet] [Doctoral dissertation]. University of Florida; 2010. [cited 2019 Sep 21]. Available from: http://ufdc.ufl.edu/UFE0041145.

Council of Science Editors:

Buta E. Computational Approaches for Empirical Bayes Methods and Bayesian Sensitivity Analysis. [Doctoral Dissertation]. University of Florida; 2010. Available from: http://ufdc.ufl.edu/UFE0041145


University of Florida

4. Tan, Aixin. Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models.

Degree: PhD, Statistics, 2009, University of Florida

 Markov chain Monte Carlo (MCMC) methods have received considerable attention as powerful computing tools in Bayesian statistical analysis. The idea is to produce Markov chain… (more)

Subjects/Keywords: Consistent estimators; Ergodic theory; Estimators; Markov chains; Perceptron convergence procedure; Simulations; Standard error; Statistics; Sufficient conditions; Tours; asymptotic, convergence, drift, ergodicity, geometric, minorization, variance

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APA (6th Edition):

Tan, A. (2009). Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0024910

Chicago Manual of Style (16th Edition):

Tan, Aixin. “Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models.” 2009. Doctoral Dissertation, University of Florida. Accessed September 21, 2019. http://ufdc.ufl.edu/UFE0024910.

MLA Handbook (7th Edition):

Tan, Aixin. “Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models.” 2009. Web. 21 Sep 2019.

Vancouver:

Tan A. Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models. [Internet] [Doctoral dissertation]. University of Florida; 2009. [cited 2019 Sep 21]. Available from: http://ufdc.ufl.edu/UFE0024910.

Council of Science Editors:

Tan A. Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models. [Doctoral Dissertation]. University of Florida; 2009. Available from: http://ufdc.ufl.edu/UFE0024910


University of Florida

5. Goswami, Manisha. American Option Pricing under Stochastic Volatility Empirical Evaluations.

Degree: PhD, Industrial and Systems Engineering, 2008, University of Florida

 Over the past few years, model complexity in quantitative finance has increased substantially in response to earlier models that did not capture critical events for… (more)

Subjects/Keywords: American option; Approximation; Assets; Hedging; Market prices; Modeling; Parametric models; Prices; Pricing; Stochastic models; american, lsm, stochastic, volatility

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Goswami, M. (2008). American Option Pricing under Stochastic Volatility Empirical Evaluations. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0022624

Chicago Manual of Style (16th Edition):

Goswami, Manisha. “American Option Pricing under Stochastic Volatility Empirical Evaluations.” 2008. Doctoral Dissertation, University of Florida. Accessed September 21, 2019. http://ufdc.ufl.edu/UFE0022624.

MLA Handbook (7th Edition):

Goswami, Manisha. “American Option Pricing under Stochastic Volatility Empirical Evaluations.” 2008. Web. 21 Sep 2019.

Vancouver:

Goswami M. American Option Pricing under Stochastic Volatility Empirical Evaluations. [Internet] [Doctoral dissertation]. University of Florida; 2008. [cited 2019 Sep 21]. Available from: http://ufdc.ufl.edu/UFE0022624.

Council of Science Editors:

Goswami M. American Option Pricing under Stochastic Volatility Empirical Evaluations. [Doctoral Dissertation]. University of Florida; 2008. Available from: http://ufdc.ufl.edu/UFE0022624


University of Florida

6. Guha, Suchandan. American Option Pricing under Stochastic Volatility Efficient Numerical Approaches.

Degree: PhD, Industrial and Systems Engineering, 2008, University of Florida

 We developed two new numerical techniques to price American options when the underlying follows a bivariate process. The first technique exploits the semi-martingale representation of… (more)

Subjects/Keywords: American option; Approximation; Call options; Finance; Mathematical constants; Prices; Pricing; Put options; Stochastic models; Stock prices; american, engineering, financial, lsm, option, pricing, stochastic, volatility

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APA (6th Edition):

Guha, S. (2008). American Option Pricing under Stochastic Volatility Efficient Numerical Approaches. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0022641

Chicago Manual of Style (16th Edition):

Guha, Suchandan. “American Option Pricing under Stochastic Volatility Efficient Numerical Approaches.” 2008. Doctoral Dissertation, University of Florida. Accessed September 21, 2019. http://ufdc.ufl.edu/UFE0022641.

MLA Handbook (7th Edition):

Guha, Suchandan. “American Option Pricing under Stochastic Volatility Efficient Numerical Approaches.” 2008. Web. 21 Sep 2019.

Vancouver:

Guha S. American Option Pricing under Stochastic Volatility Efficient Numerical Approaches. [Internet] [Doctoral dissertation]. University of Florida; 2008. [cited 2019 Sep 21]. Available from: http://ufdc.ufl.edu/UFE0022641.

Council of Science Editors:

Guha S. American Option Pricing under Stochastic Volatility Efficient Numerical Approaches. [Doctoral Dissertation]. University of Florida; 2008. Available from: http://ufdc.ufl.edu/UFE0022641


University of Florida

7. Roy, Vivekananda. Theoretical and Methodological Developments for Markov Chain Monte Carlo Algorithms for Bayesian Regression.

Degree: PhD, Statistics, 2008, University of Florida

 I develop theoretical and methodological results for Markov chain Monte Carlo (MCMC) algorithms for two different Bayesian regression models. First, I consider a probit regression… (more)

Subjects/Keywords: Consistent estimators; Ergodic theory; Markov chains; Mathematical theorems; Matrices; Perceptron convergence procedure; Regression analysis; Simulations; Statistical discrepancies; Statistics; bayesian, da, efficiency, markov, monte, multivariate, probit, px, regenerative

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Roy, V. (2008). Theoretical and Methodological Developments for Markov Chain Monte Carlo Algorithms for Bayesian Regression. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0022377

Chicago Manual of Style (16th Edition):

Roy, Vivekananda. “Theoretical and Methodological Developments for Markov Chain Monte Carlo Algorithms for Bayesian Regression.” 2008. Doctoral Dissertation, University of Florida. Accessed September 21, 2019. http://ufdc.ufl.edu/UFE0022377.

MLA Handbook (7th Edition):

Roy, Vivekananda. “Theoretical and Methodological Developments for Markov Chain Monte Carlo Algorithms for Bayesian Regression.” 2008. Web. 21 Sep 2019.

Vancouver:

Roy V. Theoretical and Methodological Developments for Markov Chain Monte Carlo Algorithms for Bayesian Regression. [Internet] [Doctoral dissertation]. University of Florida; 2008. [cited 2019 Sep 21]. Available from: http://ufdc.ufl.edu/UFE0022377.

Council of Science Editors:

Roy V. Theoretical and Methodological Developments for Markov Chain Monte Carlo Algorithms for Bayesian Regression. [Doctoral Dissertation]. University of Florida; 2008. Available from: http://ufdc.ufl.edu/UFE0022377


University of Florida

8. Li, Zhen. Bayesian Methodologies for Genomic Data with Missing Covariates.

Degree: PhD, Statistics, 2008, University of Florida

 With advancing technology, large single nucleotide polymorphism (SNP) datasets are easily available. For the ADEPT 2 project, we have candidate SNPs and interesting phenotypic trait… (more)

Subjects/Keywords: Bayes estimators; Covariance; Datasets; Genotypes; Markov chains; Matrices; Missing data; Modeling; Parametric models; Parents; bayes, genomic, gibbs, metropolis, missing, variable

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Li, Z. (2008). Bayesian Methodologies for Genomic Data with Missing Covariates. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0022460

Chicago Manual of Style (16th Edition):

Li, Zhen. “Bayesian Methodologies for Genomic Data with Missing Covariates.” 2008. Doctoral Dissertation, University of Florida. Accessed September 21, 2019. http://ufdc.ufl.edu/UFE0022460.

MLA Handbook (7th Edition):

Li, Zhen. “Bayesian Methodologies for Genomic Data with Missing Covariates.” 2008. Web. 21 Sep 2019.

Vancouver:

Li Z. Bayesian Methodologies for Genomic Data with Missing Covariates. [Internet] [Doctoral dissertation]. University of Florida; 2008. [cited 2019 Sep 21]. Available from: http://ufdc.ufl.edu/UFE0022460.

Council of Science Editors:

Li Z. Bayesian Methodologies for Genomic Data with Missing Covariates. [Doctoral Dissertation]. University of Florida; 2008. Available from: http://ufdc.ufl.edu/UFE0022460

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