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You searched for +publisher:"Texas A&M University" +contributor:("Lahiri, Soumendra"). Showing records 1 – 10 of 10 total matches.

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Texas A&M University

1. Roychowdhury, Lakshmi 1975-. Optimal Points for a Probability Distribution on a Nonhomogeneous Cantor Set.

Degree: 2012, Texas A&M University

 The objective of my thesis is to find optimal points and the quantization error for a probability measure defined on a Cantor set. The Cantor… (more)

Subjects/Keywords: Cantor set; quantization error; Optimal points

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

APA (6th Edition):

Roychowdhury, L. 1. (2012). Optimal Points for a Probability Distribution on a Nonhomogeneous Cantor Set. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/149228

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):

Roychowdhury, Lakshmi 1975-. “Optimal Points for a Probability Distribution on a Nonhomogeneous Cantor Set.” 2012. Thesis, Texas A&M University. Accessed April 04, 2020. http://hdl.handle.net/1969.1/149228.

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

MLA Handbook (7th Edition):

Roychowdhury, Lakshmi 1975-. “Optimal Points for a Probability Distribution on a Nonhomogeneous Cantor Set.” 2012. Web. 04 Apr 2020.

Vancouver:

Roychowdhury L1. Optimal Points for a Probability Distribution on a Nonhomogeneous Cantor Set. [Internet] [Thesis]. Texas A&M University; 2012. [cited 2020 Apr 04]. Available from: http://hdl.handle.net/1969.1/149228.

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

Council of Science Editors:

Roychowdhury L1. Optimal Points for a Probability Distribution on a Nonhomogeneous Cantor Set. [Thesis]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/149228

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


Texas A&M University

2. Gregory, Karl Bruce. Two-Sample Testing in High Dimension and a Smooth Block Bootstrap for Time Series.

Degree: 2014, Texas A&M University

 This document contains three sections. The first two present new methods for two-sample testing where there are many variables of interest and the third presents… (more)

Subjects/Keywords: high dimension; bootstrap; two-sample

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APA (6th Edition):

Gregory, K. B. (2014). Two-Sample Testing in High Dimension and a Smooth Block Bootstrap for Time Series. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/153324

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):

Gregory, Karl Bruce. “Two-Sample Testing in High Dimension and a Smooth Block Bootstrap for Time Series.” 2014. Thesis, Texas A&M University. Accessed April 04, 2020. http://hdl.handle.net/1969.1/153324.

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

MLA Handbook (7th Edition):

Gregory, Karl Bruce. “Two-Sample Testing in High Dimension and a Smooth Block Bootstrap for Time Series.” 2014. Web. 04 Apr 2020.

Vancouver:

Gregory KB. Two-Sample Testing in High Dimension and a Smooth Block Bootstrap for Time Series. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2020 Apr 04]. Available from: http://hdl.handle.net/1969.1/153324.

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

Council of Science Editors:

Gregory KB. Two-Sample Testing in High Dimension and a Smooth Block Bootstrap for Time Series. [Thesis]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/153324

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


Texas A&M University

3. Lee, Seokho. Principal Components Analysis for Binary Data.

Degree: 2010, Texas A&M University

 Principal components analysis (PCA) has been widely used as a statistical tool for the dimension reduction of multivariate data in various application areas and extensively… (more)

Subjects/Keywords: BINARY DATA; DIMENSION REDUCTION; MM ALGORITHM; LASSO; PCA; REGULARIZATION; SPARSITY; VARIATIONAL METHOD

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APA (6th Edition):

Lee, S. (2010). Principal Components Analysis for Binary Data. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2009-05-602

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):

Lee, Seokho. “Principal Components Analysis for Binary Data.” 2010. Thesis, Texas A&M University. Accessed April 04, 2020. http://hdl.handle.net/1969.1/ETD-TAMU-2009-05-602.

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

MLA Handbook (7th Edition):

Lee, Seokho. “Principal Components Analysis for Binary Data.” 2010. Web. 04 Apr 2020.

Vancouver:

Lee S. Principal Components Analysis for Binary Data. [Internet] [Thesis]. Texas A&M University; 2010. [cited 2020 Apr 04]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-05-602.

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

Council of Science Editors:

Lee S. Principal Components Analysis for Binary Data. [Thesis]. Texas A&M University; 2010. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-05-602

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


Texas A&M University

4. Lee, Jun Bum. A Novel Approach to the Analysis of Nonlinear Time Series with Applications to Financial Data.

Degree: 2012, Texas A&M University

 The spectral analysis method is an important tool in time series analysis and the spectral density plays a crucial role on the spectral analysis. However,… (more)

Subjects/Keywords: goodness-of-fit test; nonlinear time series; quantile spectral density; association spectral density

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

APA (6th Edition):

Lee, J. B. (2012). A Novel Approach to the Analysis of Nonlinear Time Series with Applications to Financial Data. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10928

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):

Lee, Jun Bum. “A Novel Approach to the Analysis of Nonlinear Time Series with Applications to Financial Data.” 2012. Thesis, Texas A&M University. Accessed April 04, 2020. http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10928.

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

MLA Handbook (7th Edition):

Lee, Jun Bum. “A Novel Approach to the Analysis of Nonlinear Time Series with Applications to Financial Data.” 2012. Web. 04 Apr 2020.

Vancouver:

Lee JB. A Novel Approach to the Analysis of Nonlinear Time Series with Applications to Financial Data. [Internet] [Thesis]. Texas A&M University; 2012. [cited 2020 Apr 04]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10928.

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

Council of Science Editors:

Lee JB. A Novel Approach to the Analysis of Nonlinear Time Series with Applications to Financial Data. [Thesis]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10928

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


Texas A&M University

5. Mukhopadhyay, Subhadeep. Nonparametric Inference for High Dimensional Data.

Degree: 2013, Texas A&M University

 Learning from data, especially ?Big Data?, is becoming increasingly popular under names such as Data Mining, Data Science, Machine Learning, Statistical Learning and High Dimensional… (more)

Subjects/Keywords: Big data; Quantile; Empirical Likelihood; LP score function; Copula; Nonparametric; Classification; Data Science

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

APA (6th Edition):

Mukhopadhyay, S. (2013). Nonparametric Inference for High Dimensional Data. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/149430

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):

Mukhopadhyay, Subhadeep. “Nonparametric Inference for High Dimensional Data.” 2013. Thesis, Texas A&M University. Accessed April 04, 2020. http://hdl.handle.net/1969.1/149430.

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

MLA Handbook (7th Edition):

Mukhopadhyay, Subhadeep. “Nonparametric Inference for High Dimensional Data.” 2013. Web. 04 Apr 2020.

Vancouver:

Mukhopadhyay S. Nonparametric Inference for High Dimensional Data. [Internet] [Thesis]. Texas A&M University; 2013. [cited 2020 Apr 04]. Available from: http://hdl.handle.net/1969.1/149430.

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

Council of Science Editors:

Mukhopadhyay S. Nonparametric Inference for High Dimensional Data. [Thesis]. Texas A&M University; 2013. Available from: http://hdl.handle.net/1969.1/149430

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


Texas A&M University

6. Song, Qifan. Variable Selection for Ultra High Dimensional Data.

Degree: 2014, Texas A&M University

 Variable selection plays an important role for the high dimensional data analysis. In this work, we first propose a Bayesian variable selection approach for ultra-high… (more)

Subjects/Keywords: High Dimensional Variable Selection; Big Data; Penalized Likelihood Approach; Posterior Consistency

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

APA (6th Edition):

Song, Q. (2014). Variable Selection for Ultra High Dimensional Data. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/153224

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):

Song, Qifan. “Variable Selection for Ultra High Dimensional Data.” 2014. Thesis, Texas A&M University. Accessed April 04, 2020. http://hdl.handle.net/1969.1/153224.

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

MLA Handbook (7th Edition):

Song, Qifan. “Variable Selection for Ultra High Dimensional Data.” 2014. Web. 04 Apr 2020.

Vancouver:

Song Q. Variable Selection for Ultra High Dimensional Data. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2020 Apr 04]. Available from: http://hdl.handle.net/1969.1/153224.

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

Council of Science Editors:

Song Q. Variable Selection for Ultra High Dimensional Data. [Thesis]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/153224

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


Texas A&M University

7. Maity, Arnab. Efficient inference in general semiparametric regression models.

Degree: 2009, Texas A&M University

 Semiparametric regression has become very popular in the field of Statistics over the years. While on one hand more and more sophisticated models are being… (more)

Subjects/Keywords: Nonparametric/Semiparametric Regression; Kernel Method; Measurement Error; Semiparametric Efficiency; Repeated Measures

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

APA (6th Edition):

Maity, A. (2009). Efficient inference in general semiparametric regression models. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-3075

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):

Maity, Arnab. “Efficient inference in general semiparametric regression models.” 2009. Thesis, Texas A&M University. Accessed April 04, 2020. http://hdl.handle.net/1969.1/ETD-TAMU-3075.

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

MLA Handbook (7th Edition):

Maity, Arnab. “Efficient inference in general semiparametric regression models.” 2009. Web. 04 Apr 2020.

Vancouver:

Maity A. Efficient inference in general semiparametric regression models. [Internet] [Thesis]. Texas A&M University; 2009. [cited 2020 Apr 04]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-3075.

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

Council of Science Editors:

Maity A. Efficient inference in general semiparametric regression models. [Thesis]. Texas A&M University; 2009. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-3075

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

8. Rister, Krista Dianne. Resampling Methodology in Spatial Prediction and Repeated Measures Time Series.

Degree: 2012, Texas A&M University

 In recent years, the application of resampling methods to dependent data, such as time series or spatial data, has been a growing field in the… (more)

Subjects/Keywords: bootstrapping; Kriging; spatial statistics; time series; spatial prediction; covariance parameters

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

APA (6th Edition):

Rister, K. D. (2012). Resampling Methodology in Spatial Prediction and Repeated Measures Time Series. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8929

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):

Rister, Krista Dianne. “Resampling Methodology in Spatial Prediction and Repeated Measures Time Series.” 2012. Thesis, Texas A&M University. Accessed April 04, 2020. http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8929.

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

MLA Handbook (7th Edition):

Rister, Krista Dianne. “Resampling Methodology in Spatial Prediction and Repeated Measures Time Series.” 2012. Web. 04 Apr 2020.

Vancouver:

Rister KD. Resampling Methodology in Spatial Prediction and Repeated Measures Time Series. [Internet] [Thesis]. Texas A&M University; 2012. [cited 2020 Apr 04]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8929.

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

Council of Science Editors:

Rister KD. Resampling Methodology in Spatial Prediction and Repeated Measures Time Series. [Thesis]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8929

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

9. Crawford, Scott. Efficient Estimation in a Regression Model with Missing Responses.

Degree: 2012, Texas A&M University

 This article examines methods to efficiently estimate the mean response in a linear model with an unknown error distribution under the assumption that the responses… (more)

Subjects/Keywords: Efficiency; Missing at Random; Regression; Full Imputation

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

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APA (6th Edition):

Crawford, S. (2012). Efficient Estimation in a Regression Model with Missing Responses. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11396

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):

Crawford, Scott. “Efficient Estimation in a Regression Model with Missing Responses.” 2012. Thesis, Texas A&M University. Accessed April 04, 2020. http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11396.

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

MLA Handbook (7th Edition):

Crawford, Scott. “Efficient Estimation in a Regression Model with Missing Responses.” 2012. Web. 04 Apr 2020.

Vancouver:

Crawford S. Efficient Estimation in a Regression Model with Missing Responses. [Internet] [Thesis]. Texas A&M University; 2012. [cited 2020 Apr 04]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11396.

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

Council of Science Editors:

Crawford S. Efficient Estimation in a Regression Model with Missing Responses. [Thesis]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11396

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

10. Bandyopadhyay, Soutir. On Parametric and Nonparametric Methods for Dependent Data.

Degree: 2011, Texas A&M University

 In recent years, there has been a surge of research interest in the analysis of time series and spatial data. While on one hand more… (more)

Subjects/Keywords: Bootstrap; Mean squared prediction error; second order bias correction; tilting; Asymptotic independence; Central limit theorem; DFT; Random field; Spatial processes; Spatial design.

Page 1 Page 2 Page 3 Page 4 Page 5

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

APA (6th Edition):

Bandyopadhyay, S. (2011). On Parametric and Nonparametric Methods for Dependent Data. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8277

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):

Bandyopadhyay, Soutir. “On Parametric and Nonparametric Methods for Dependent Data.” 2011. Thesis, Texas A&M University. Accessed April 04, 2020. http://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8277.

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

MLA Handbook (7th Edition):

Bandyopadhyay, Soutir. “On Parametric and Nonparametric Methods for Dependent Data.” 2011. Web. 04 Apr 2020.

Vancouver:

Bandyopadhyay S. On Parametric and Nonparametric Methods for Dependent Data. [Internet] [Thesis]. Texas A&M University; 2011. [cited 2020 Apr 04]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8277.

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

Council of Science Editors:

Bandyopadhyay S. On Parametric and Nonparametric Methods for Dependent Data. [Thesis]. Texas A&M University; 2011. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8277

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

.