Advanced search options

Sorted by: relevance · author · university · date | New search

You searched for `+publisher:"The Ohio State University" +contributor:("Herbei, Radu")`

.
Showing records 1 – 8 of
8 total matches.

▼ Search Limiters

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

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1531833318013379

► 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

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1534335592622713

► 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

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406

► 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

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1433771825

► 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

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1406912247

► 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

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1437399395

► 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

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1562966204796479

► 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…

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1372173121

► 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…

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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