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You searched for subject:(Smoothing Parameters). Showing records 1 – 5 of 5 total matches.

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University of Rochester

1. Grzesik, Katherine. Local Cross-Validated Smoothing Parameter Estimation for Linear Smoothers.

Degree: PhD, 2018, University of Rochester

 Nonparametrically estimating a regression function with varying degrees of smoothness or heteroscedasticity can benefit from a smoother that uses a data-adaptive smoothing parameter function to… (more)

Subjects/Keywords: Smoothing; Nonparametric; Cross validation; Local parameters

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

APA (6th Edition):

Grzesik, K. (2018). Local Cross-Validated Smoothing Parameter Estimation for Linear Smoothers. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/33393

Chicago Manual of Style (16th Edition):

Grzesik, Katherine. “Local Cross-Validated Smoothing Parameter Estimation for Linear Smoothers.” 2018. Doctoral Dissertation, University of Rochester. Accessed July 16, 2019. http://hdl.handle.net/1802/33393.

MLA Handbook (7th Edition):

Grzesik, Katherine. “Local Cross-Validated Smoothing Parameter Estimation for Linear Smoothers.” 2018. Web. 16 Jul 2019.

Vancouver:

Grzesik K. Local Cross-Validated Smoothing Parameter Estimation for Linear Smoothers. [Internet] [Doctoral dissertation]. University of Rochester; 2018. [cited 2019 Jul 16]. Available from: http://hdl.handle.net/1802/33393.

Council of Science Editors:

Grzesik K. Local Cross-Validated Smoothing Parameter Estimation for Linear Smoothers. [Doctoral Dissertation]. University of Rochester; 2018. Available from: http://hdl.handle.net/1802/33393


University of Illinois – Urbana-Champaign

2. Narasingaraj, Harish Balaji. Optimizing smoothing parameters for the triple exponential forecasting model.

Degree: MS, Industrial Engineering, 2016, University of Illinois – Urbana-Champaign

 Exponential smoothing has always been a popular topic of research in forecasting. The triple exponential smoothing in particular involves modeling a function that is a… (more)

Subjects/Keywords: Holt Winters; Triple Exponential Smoothing parameters; M3 Competition

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APA (6th Edition):

Narasingaraj, H. B. (2016). Optimizing smoothing parameters for the triple exponential forecasting model. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/90834

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Narasingaraj, Harish Balaji. “Optimizing smoothing parameters for the triple exponential forecasting model.” 2016. Thesis, University of Illinois – Urbana-Champaign. Accessed July 16, 2019. http://hdl.handle.net/2142/90834.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Narasingaraj, Harish Balaji. “Optimizing smoothing parameters for the triple exponential forecasting model.” 2016. Web. 16 Jul 2019.

Vancouver:

Narasingaraj HB. Optimizing smoothing parameters for the triple exponential forecasting model. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 Jul 16]. Available from: http://hdl.handle.net/2142/90834.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Narasingaraj HB. Optimizing smoothing parameters for the triple exponential forecasting model. [Thesis]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/90834

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Georgia

3. Sun, Xiaoxiao. Nonparametric methods for big and complex datasets under a reproducing kernel Hilbert space framework.

Degree: PhD, Statistics, 2018, University of Georgia

 Large and complex data have been generated routinely from various sources, for instance, time course biological studies and social media. Classic nonparametric models, such as… (more)

Subjects/Keywords: smoothing spline ANOVA; smoothing parameters selection; optimal smoothing parameters; function-on-function regression; representer theorem; penalized least squares; reproducing kernel Hilbert space; minimax convergence rate; time course RNA-seq; differentially expressed genes

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APA (6th Edition):

Sun, X. (2018). Nonparametric methods for big and complex datasets under a reproducing kernel Hilbert space framework. (Doctoral Dissertation). University of Georgia. Retrieved from http://hdl.handle.net/10724/38552

Chicago Manual of Style (16th Edition):

Sun, Xiaoxiao. “Nonparametric methods for big and complex datasets under a reproducing kernel Hilbert space framework.” 2018. Doctoral Dissertation, University of Georgia. Accessed July 16, 2019. http://hdl.handle.net/10724/38552.

MLA Handbook (7th Edition):

Sun, Xiaoxiao. “Nonparametric methods for big and complex datasets under a reproducing kernel Hilbert space framework.” 2018. Web. 16 Jul 2019.

Vancouver:

Sun X. Nonparametric methods for big and complex datasets under a reproducing kernel Hilbert space framework. [Internet] [Doctoral dissertation]. University of Georgia; 2018. [cited 2019 Jul 16]. Available from: http://hdl.handle.net/10724/38552.

Council of Science Editors:

Sun X. Nonparametric methods for big and complex datasets under a reproducing kernel Hilbert space framework. [Doctoral Dissertation]. University of Georgia; 2018. Available from: http://hdl.handle.net/10724/38552


Texas A&M University

4. Xu, Ganggang. Variable Selection and Function Estimation Using Penalized Methods.

Degree: 2012, Texas A&M University

 Penalized methods are becoming more and more popular in statistical research. This dissertation research covers two major aspects of applications of penalized methods: variable selection… (more)

Subjects/Keywords: Adaptive lasso; Autoregressive model; Infinite variance; Least absolute deviation; Cross-validation, Generalized estimating equations, Multiple smoothing parameters, Penalized splines, Working covariance matrix.

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APA (6th Edition):

Xu, G. (2012). Variable Selection and Function Estimation Using Penalized Methods. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10451

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Xu, Ganggang. “Variable Selection and Function Estimation Using Penalized Methods.” 2012. Thesis, Texas A&M University. Accessed July 16, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10451.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Xu, Ganggang. “Variable Selection and Function Estimation Using Penalized Methods.” 2012. Web. 16 Jul 2019.

Vancouver:

Xu G. Variable Selection and Function Estimation Using Penalized Methods. [Internet] [Thesis]. Texas A&M University; 2012. [cited 2019 Jul 16]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10451.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Xu G. Variable Selection and Function Estimation Using Penalized Methods. [Thesis]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10451

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

5. Marcos Antonio de Lima Santos. Taxas de SobrevivÃncia de Participantes de Fundos de PensÃo Vinculados ao Setor ElÃtrico Nacional.

Degree: Master, 2011, Universidade Federal do Ceará

Esta dissertaÃÃo tem por objetivo calcular as taxas de sobrevivÃncia dos participantes de Fundos de PensÃo do setor elÃtrico nacional, bem como encontrar o modelo… (more)

Subjects/Keywords: CIENCIAS SOCIAIS APLICADAS; Fundos de PensÃo; Taxas de SobrevivÃncia; Modelos ParamÃtricos; SuavizaÃÃo; ParÃmetros; Pension Funds; Survival Rates; Parametric Models; Smoothing Parameters; Fundos de PensÃo

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APA (6th Edition):

Santos, M. A. d. L. (2011). Taxas de SobrevivÃncia de Participantes de Fundos de PensÃo Vinculados ao Setor ElÃtrico Nacional. (Masters Thesis). Universidade Federal do Ceará. Retrieved from http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=6675 ;

Chicago Manual of Style (16th Edition):

Santos, Marcos Antonio de Lima. “Taxas de SobrevivÃncia de Participantes de Fundos de PensÃo Vinculados ao Setor ElÃtrico Nacional.” 2011. Masters Thesis, Universidade Federal do Ceará. Accessed July 16, 2019. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=6675 ;.

MLA Handbook (7th Edition):

Santos, Marcos Antonio de Lima. “Taxas de SobrevivÃncia de Participantes de Fundos de PensÃo Vinculados ao Setor ElÃtrico Nacional.” 2011. Web. 16 Jul 2019.

Vancouver:

Santos MAdL. Taxas de SobrevivÃncia de Participantes de Fundos de PensÃo Vinculados ao Setor ElÃtrico Nacional. [Internet] [Masters thesis]. Universidade Federal do Ceará 2011. [cited 2019 Jul 16]. Available from: http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=6675 ;.

Council of Science Editors:

Santos MAdL. Taxas de SobrevivÃncia de Participantes de Fundos de PensÃo Vinculados ao Setor ElÃtrico Nacional. [Masters Thesis]. Universidade Federal do Ceará 2011. Available from: http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=6675 ;

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