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You searched for +publisher:"The Ohio State University" +contributor:("Herbei, Radu"). Showing records 1 – 8 of 8 total matches.

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The Ohio State University

1. Smith, Corey James. Exact Markov Chain Monte Carlo with Likelihood Approximations for Functional Linear Models.

Degree: PhD, Statistics, 2018, The Ohio State University

 Functional data analysis is a branch of statistics that deals with the theory and analysis of data which may be comprised of functions in addition… (more)

Subjects/Keywords: Statistics; Exact MCMC; Bernoulli factory; functional data; Monte Carlo; Bayesian

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APA (6th Edition):

Smith, C. J. (2018). Exact Markov Chain Monte Carlo with Likelihood Approximations for Functional Linear Models. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1531833318013379

Chicago Manual of Style (16th Edition):

Smith, Corey James. “Exact Markov Chain Monte Carlo with Likelihood Approximations for Functional Linear Models.” 2018. Doctoral Dissertation, The Ohio State University. Accessed January 17, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531833318013379.

MLA Handbook (7th Edition):

Smith, Corey James. “Exact Markov Chain Monte Carlo with Likelihood Approximations for Functional Linear Models.” 2018. Web. 17 Jan 2021.

Vancouver:

Smith CJ. Exact Markov Chain Monte Carlo with Likelihood Approximations for Functional Linear Models. [Internet] [Doctoral dissertation]. The Ohio State University; 2018. [cited 2021 Jan 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1531833318013379.

Council of Science Editors:

Smith CJ. Exact Markov Chain Monte Carlo with Likelihood Approximations for Functional Linear Models. [Doctoral Dissertation]. The Ohio State University; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1531833318013379


The Ohio State University

2. Awasthi, Achal. Parameter Estimation in Stochastic Volatility Models Via Approximate Bayesian Computing.

Degree: MS, Statistics, 2018, The Ohio State University

 In this thesis, we propose a generalized Heston model as a tool to estimate volatility.We have used Approximate Bayesian Computing to estimate the parameters ofthe… (more)

Subjects/Keywords: Statistics; Stochastic Volatility, Emerging Markets, Approximate Bayesian Computing

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APA (6th Edition):

Awasthi, A. (2018). Parameter Estimation in Stochastic Volatility Models Via Approximate Bayesian Computing. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1534335592622713

Chicago Manual of Style (16th Edition):

Awasthi, Achal. “Parameter Estimation in Stochastic Volatility Models Via Approximate Bayesian Computing.” 2018. Masters Thesis, The Ohio State University. Accessed January 17, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534335592622713.

MLA Handbook (7th Edition):

Awasthi, Achal. “Parameter Estimation in Stochastic Volatility Models Via Approximate Bayesian Computing.” 2018. Web. 17 Jan 2021.

Vancouver:

Awasthi A. Parameter Estimation in Stochastic Volatility Models Via Approximate Bayesian Computing. [Internet] [Masters thesis]. The Ohio State University; 2018. [cited 2021 Jan 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1534335592622713.

Council of Science Editors:

Awasthi A. Parameter Estimation in Stochastic Volatility Models Via Approximate Bayesian Computing. [Masters Thesis]. The Ohio State University; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1534335592622713


The Ohio State University

3. Olsen, Andrew Nolan. When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods.

Degree: PhD, Statistics, 2015, The Ohio State University

 Markov chains are an incredibly powerful tool for statisticians and other practitioners. They allow for random draws, though autocorrelated, to be obtained from a vast… (more)

Subjects/Keywords: Statistics; Markov chain Monte Carlo convergence; Markov chain Monte Carlo standard errors; geometric ergodicity; scale-usage heterogeneity

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APA (6th Edition):

Olsen, A. N. (2015). When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406

Chicago Manual of Style (16th Edition):

Olsen, Andrew Nolan. “When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods.” 2015. Doctoral Dissertation, The Ohio State University. Accessed January 17, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406.

MLA Handbook (7th Edition):

Olsen, Andrew Nolan. “When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods.” 2015. Web. 17 Jan 2021.

Vancouver:

Olsen AN. When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods. [Internet] [Doctoral dissertation]. The Ohio State University; 2015. [cited 2021 Jan 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406.

Council of Science Editors:

Olsen AN. When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods. [Doctoral Dissertation]. The Ohio State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406


The Ohio State University

4. White, Staci A. Quantifying Model Error in Bayesian Parameter Estimation.

Degree: PhD, Statistics, 2015, The Ohio State University

 As technological power increases, statistical models are becoming increasing complex. In a Bayesian analysis, performing parametric inference typically requires exploring the posterior distribution using Markov… (more)

Subjects/Keywords: Statistics; Model Error; Bayesian Estimation; Hierarchical Model; Approximation; Kullback-Leibler; divergence

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APA (6th Edition):

White, S. A. (2015). Quantifying Model Error in Bayesian Parameter Estimation. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1433771825

Chicago Manual of Style (16th Edition):

White, Staci A. “Quantifying Model Error in Bayesian Parameter Estimation.” 2015. Doctoral Dissertation, The Ohio State University. Accessed January 17, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1433771825.

MLA Handbook (7th Edition):

White, Staci A. “Quantifying Model Error in Bayesian Parameter Estimation.” 2015. Web. 17 Jan 2021.

Vancouver:

White SA. Quantifying Model Error in Bayesian Parameter Estimation. [Internet] [Doctoral dissertation]. The Ohio State University; 2015. [cited 2021 Jan 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1433771825.

Council of Science Editors:

White SA. Quantifying Model Error in Bayesian Parameter Estimation. [Doctoral Dissertation]. The Ohio State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1433771825


The Ohio State University

5. Schneider, Grant W. Maximum Likelihood Estimation for Stochastic Differential Equations Using Sequential Kriging-Based Optimization.

Degree: PhD, Statistics, 2014, The Ohio State University

 Stochastic differential equations (SDEs) are used as statistical models in many disciplines. However, intractable likelihood functions for SDEs make inference challenging, and we need to… (more)

Subjects/Keywords: Statistics; Discretely sampled diffusions; Expected improvement; Gaussian process; Sequential Monte Carlo; Parameter estimation

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APA (6th Edition):

Schneider, G. W. (2014). Maximum Likelihood Estimation for Stochastic Differential Equations Using Sequential Kriging-Based Optimization. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1406912247

Chicago Manual of Style (16th Edition):

Schneider, Grant W. “Maximum Likelihood Estimation for Stochastic Differential Equations Using Sequential Kriging-Based Optimization.” 2014. Doctoral Dissertation, The Ohio State University. Accessed January 17, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406912247.

MLA Handbook (7th Edition):

Schneider, Grant W. “Maximum Likelihood Estimation for Stochastic Differential Equations Using Sequential Kriging-Based Optimization.” 2014. Web. 17 Jan 2021.

Vancouver:

Schneider GW. Maximum Likelihood Estimation for Stochastic Differential Equations Using Sequential Kriging-Based Optimization. [Internet] [Doctoral dissertation]. The Ohio State University; 2014. [cited 2021 Jan 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1406912247.

Council of Science Editors:

Schneider GW. Maximum Likelihood Estimation for Stochastic Differential Equations Using Sequential Kriging-Based Optimization. [Doctoral Dissertation]. The Ohio State University; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1406912247

6. Gendre, Victor Hugues. Predicting short term exchange rates with Bayesian autoregressive state space models: an investigation of the Metropolis Hastings algorithm forecasting efficiency.

Degree: MS, Statistics, 2015, The Ohio State University

 In this thesis, we propose four Bayesian AR(1) models, namely an AR(1) with constant variance and constant slope coefficient, an AR(1) with stochastic variance and… (more)

Subjects/Keywords: Statistics

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APA (6th Edition):

Gendre, V. H. (2015). Predicting short term exchange rates with Bayesian autoregressive state space models: an investigation of the Metropolis Hastings algorithm forecasting efficiency. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1437399395

Chicago Manual of Style (16th Edition):

Gendre, Victor Hugues. “Predicting short term exchange rates with Bayesian autoregressive state space models: an investigation of the Metropolis Hastings algorithm forecasting efficiency.” 2015. Masters Thesis, The Ohio State University. Accessed January 17, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1437399395.

MLA Handbook (7th Edition):

Gendre, Victor Hugues. “Predicting short term exchange rates with Bayesian autoregressive state space models: an investigation of the Metropolis Hastings algorithm forecasting efficiency.” 2015. Web. 17 Jan 2021.

Vancouver:

Gendre VH. Predicting short term exchange rates with Bayesian autoregressive state space models: an investigation of the Metropolis Hastings algorithm forecasting efficiency. [Internet] [Masters thesis]. The Ohio State University; 2015. [cited 2021 Jan 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1437399395.

Council of Science Editors:

Gendre VH. Predicting short term exchange rates with Bayesian autoregressive state space models: an investigation of the Metropolis Hastings algorithm forecasting efficiency. [Masters Thesis]. The Ohio State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1437399395

7. Liu, Ge. Statistical Inference for Multivariate Stochastic Differential Equations.

Degree: PhD, Statistics, 2019, The Ohio State University

 Multivariate stochastic differential equations (MVSDEs) are commonly used in many applications in fields such as biology, economics, mathematical finance, oceanography and many other scientific areas.… (more)

Subjects/Keywords: Statistics; multivariate stochastic differential equations; data imputation; Bayesian data augmentation method; Bayesian MCMC; pseudo marginal MCMC; stochastic process

…Graduate Teaching Associate, Department of Statistics, The Ohio State University 2017-present… …Graduate Research Associate, Department of Statistics, The Ohio State University Fields of Study… 

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7 Sample image

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

APA (6th Edition):

Liu, G. (2019). Statistical Inference for Multivariate Stochastic Differential Equations. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1562966204796479

Chicago Manual of Style (16th Edition):

Liu, Ge. “Statistical Inference for Multivariate Stochastic Differential Equations.” 2019. Doctoral Dissertation, The Ohio State University. Accessed January 17, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1562966204796479.

MLA Handbook (7th Edition):

Liu, Ge. “Statistical Inference for Multivariate Stochastic Differential Equations.” 2019. Web. 17 Jan 2021.

Vancouver:

Liu G. Statistical Inference for Multivariate Stochastic Differential Equations. [Internet] [Doctoral dissertation]. The Ohio State University; 2019. [cited 2021 Jan 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1562966204796479.

Council of Science Editors:

Liu G. Statistical Inference for Multivariate Stochastic Differential Equations. [Doctoral Dissertation]. The Ohio State University; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1562966204796479

8. Spade, David Allen. Investigating Convergence of Markov Chain Monte Carlo Methods for Bayesian Phylogenetic Inference.

Degree: PhD, Statistics, 2013, The Ohio State University

 In biology, it is commonly of interest to investigate the evolutionary pattern that gave rise to an existing group of individuals, such as species or… (more)

Subjects/Keywords: Statistics; Evolution and Development; Markov Chain Monte Carlo; Phylogenetics; Bayesian Inference; Convergence; Mixing Time

…program in statistics. During my six years at The Ohio State University, I have been fortunate… …M.S. Statistics, The Ohio State University 2007-present… …Graduate Teaching Associate, The Ohio State University. Fields of Study Major Field: Statistics… 

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APA (6th Edition):

Spade, D. A. (2013). Investigating Convergence of Markov Chain Monte Carlo Methods for Bayesian Phylogenetic Inference. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1372173121

Chicago Manual of Style (16th Edition):

Spade, David Allen. “Investigating Convergence of Markov Chain Monte Carlo Methods for Bayesian Phylogenetic Inference.” 2013. Doctoral Dissertation, The Ohio State University. Accessed January 17, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1372173121.

MLA Handbook (7th Edition):

Spade, David Allen. “Investigating Convergence of Markov Chain Monte Carlo Methods for Bayesian Phylogenetic Inference.” 2013. Web. 17 Jan 2021.

Vancouver:

Spade DA. Investigating Convergence of Markov Chain Monte Carlo Methods for Bayesian Phylogenetic Inference. [Internet] [Doctoral dissertation]. The Ohio State University; 2013. [cited 2021 Jan 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1372173121.

Council of Science Editors:

Spade DA. Investigating Convergence of Markov Chain Monte Carlo Methods for Bayesian Phylogenetic Inference. [Doctoral Dissertation]. The Ohio State University; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1372173121

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