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1. Shi, Xiaofeng. Large Portfolios' Risks and High-Dimensional Factor Models .

Degree: PhD, 2014, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp013x816p84p

► This dissertation explores two important topics on high-dimensional factor models. We first consider the problem of estimating and assessing the risk of a large portfolio.…
(more)

Subjects/Keywords: Factor Model; High Dimension; Penalized Estimation; Risk Management

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APA (6^{th} Edition):

Shi, X. (2014). Large Portfolios' Risks and High-Dimensional Factor Models . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp013x816p84p

Chicago Manual of Style (16^{th} Edition):

Shi, Xiaofeng. “Large Portfolios' Risks and High-Dimensional Factor Models .” 2014. Doctoral Dissertation, Princeton University. Accessed November 19, 2019. http://arks.princeton.edu/ark:/88435/dsp013x816p84p.

MLA Handbook (7^{th} Edition):

Shi, Xiaofeng. “Large Portfolios' Risks and High-Dimensional Factor Models .” 2014. Web. 19 Nov 2019.

Vancouver:

Shi X. Large Portfolios' Risks and High-Dimensional Factor Models . [Internet] [Doctoral dissertation]. Princeton University; 2014. [cited 2019 Nov 19]. Available from: http://arks.princeton.edu/ark:/88435/dsp013x816p84p.

Council of Science Editors:

Shi X. Large Portfolios' Risks and High-Dimensional Factor Models . [Doctoral Dissertation]. Princeton University; 2014. Available from: http://arks.princeton.edu/ark:/88435/dsp013x816p84p

Princeton University

2. Wang, Weichen. High-dimensional Covariance Learning .

Degree: PhD, 2016, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01nv935533t

► Massive data analyses and statistical learning in many real applications require a careful understanding of the high dimensional covariance structure. Large covariance matrix typically plays…
(more)

Subjects/Keywords: Empirical Eigen-structure; Matrix Quadratic Functionals; Principal Component Analysis; Robust Covariance Estimation; Semiparametric Factor Models

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APA (6^{th} Edition):

Wang, W. (2016). High-dimensional Covariance Learning . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01nv935533t

Chicago Manual of Style (16^{th} Edition):

Wang, Weichen. “High-dimensional Covariance Learning .” 2016. Doctoral Dissertation, Princeton University. Accessed November 19, 2019. http://arks.princeton.edu/ark:/88435/dsp01nv935533t.

MLA Handbook (7^{th} Edition):

Wang, Weichen. “High-dimensional Covariance Learning .” 2016. Web. 19 Nov 2019.

Vancouver:

Wang W. High-dimensional Covariance Learning . [Internet] [Doctoral dissertation]. Princeton University; 2016. [cited 2019 Nov 19]. Available from: http://arks.princeton.edu/ark:/88435/dsp01nv935533t.

Council of Science Editors:

Wang W. High-dimensional Covariance Learning . [Doctoral Dissertation]. Princeton University; 2016. Available from: http://arks.princeton.edu/ark:/88435/dsp01nv935533t

Princeton University

3. Wang, Yuyan. Robust High-Dimensional Regression and Factor Models .

Degree: PhD, 2016, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp019c67wq32b

► High-throughput technologies generate datasets with huge dimensionality, large sample size and heterogeneous noises. Many traditional methods become computationally infeasible or no longer applicable with these…
(more)

Subjects/Keywords: Factor models; High-dimensional linear regression; Mixture modeling of hurricane; Robust methods

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APA (6^{th} Edition):

Wang, Y. (2016). Robust High-Dimensional Regression and Factor Models . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp019c67wq32b

Chicago Manual of Style (16^{th} Edition):

Wang, Yuyan. “Robust High-Dimensional Regression and Factor Models .” 2016. Doctoral Dissertation, Princeton University. Accessed November 19, 2019. http://arks.princeton.edu/ark:/88435/dsp019c67wq32b.

MLA Handbook (7^{th} Edition):

Wang, Yuyan. “Robust High-Dimensional Regression and Factor Models .” 2016. Web. 19 Nov 2019.

Vancouver:

Wang Y. Robust High-Dimensional Regression and Factor Models . [Internet] [Doctoral dissertation]. Princeton University; 2016. [cited 2019 Nov 19]. Available from: http://arks.princeton.edu/ark:/88435/dsp019c67wq32b.

Council of Science Editors:

Wang Y. Robust High-Dimensional Regression and Factor Models . [Doctoral Dissertation]. Princeton University; 2016. Available from: http://arks.princeton.edu/ark:/88435/dsp019c67wq32b

Princeton University

4. Bose, Koushiki. Robust Dependence-Adjusted Methods for High Dimensional Data .

Degree: PhD, 2018, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01ht24wn13d

► The focus of this dissertation is the development, implementation and verification of robust methods for high dimensional heavy-tailed data, with an emphasis on underlying dependence-adjustment…
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Subjects/Keywords: Dependence Adjustment; Factor Models; High Dimensional Data; Robust Estimation; R package

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APA (6^{th} Edition):

Bose, K. (2018). Robust Dependence-Adjusted Methods for High Dimensional Data . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01ht24wn13d

Chicago Manual of Style (16^{th} Edition):

Bose, Koushiki. “Robust Dependence-Adjusted Methods for High Dimensional Data .” 2018. Doctoral Dissertation, Princeton University. Accessed November 19, 2019. http://arks.princeton.edu/ark:/88435/dsp01ht24wn13d.

MLA Handbook (7^{th} Edition):

Bose, Koushiki. “Robust Dependence-Adjusted Methods for High Dimensional Data .” 2018. Web. 19 Nov 2019.

Vancouver:

Bose K. Robust Dependence-Adjusted Methods for High Dimensional Data . [Internet] [Doctoral dissertation]. Princeton University; 2018. [cited 2019 Nov 19]. Available from: http://arks.princeton.edu/ark:/88435/dsp01ht24wn13d.

Council of Science Editors:

Bose K. Robust Dependence-Adjusted Methods for High Dimensional Data . [Doctoral Dissertation]. Princeton University; 2018. Available from: http://arks.princeton.edu/ark:/88435/dsp01ht24wn13d

Princeton University

5. Zhu, Ziwei. Distributed and Robust Statistical Learning .

Degree: PhD, 2018, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01d217qs22x

► Decentralized and corrupted data are nowadays ubiquitous, which impose fundamental challenges for modern statistical analysis. Illustrative examples are massive and decentralized data produced by distributed…
(more)

Subjects/Keywords: distributed learning; high-dimensional statistics; low-rank matrix recovery; principal component analysis; regression; robust statistics

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APA (6^{th} Edition):

Zhu, Z. (2018). Distributed and Robust Statistical Learning . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01d217qs22x

Chicago Manual of Style (16^{th} Edition):

Zhu, Ziwei. “Distributed and Robust Statistical Learning .” 2018. Doctoral Dissertation, Princeton University. Accessed November 19, 2019. http://arks.princeton.edu/ark:/88435/dsp01d217qs22x.

MLA Handbook (7^{th} Edition):

Zhu, Ziwei. “Distributed and Robust Statistical Learning .” 2018. Web. 19 Nov 2019.

Vancouver:

Zhu Z. Distributed and Robust Statistical Learning . [Internet] [Doctoral dissertation]. Princeton University; 2018. [cited 2019 Nov 19]. Available from: http://arks.princeton.edu/ark:/88435/dsp01d217qs22x.

Council of Science Editors:

Zhu Z. Distributed and Robust Statistical Learning . [Doctoral Dissertation]. Princeton University; 2018. Available from: http://arks.princeton.edu/ark:/88435/dsp01d217qs22x

6. Xia, Lucy. Statistical Methods for Complex Datasets .

Degree: PhD, 2015, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01h989r552k

► Due to the development of technology, modern datasets are evolving in terms of size and complexity. In particular, the availability of various datasets ranging from…
(more)

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APA (6^{th} Edition):

Xia, L. (2015). Statistical Methods for Complex Datasets . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01h989r552k

Chicago Manual of Style (16^{th} Edition):

Xia, Lucy. “Statistical Methods for Complex Datasets .” 2015. Doctoral Dissertation, Princeton University. Accessed November 19, 2019. http://arks.princeton.edu/ark:/88435/dsp01h989r552k.

MLA Handbook (7^{th} Edition):

Xia, Lucy. “Statistical Methods for Complex Datasets .” 2015. Web. 19 Nov 2019.

Vancouver:

Xia L. Statistical Methods for Complex Datasets . [Internet] [Doctoral dissertation]. Princeton University; 2015. [cited 2019 Nov 19]. Available from: http://arks.princeton.edu/ark:/88435/dsp01h989r552k.

Council of Science Editors:

Xia L. Statistical Methods for Complex Datasets . [Doctoral Dissertation]. Princeton University; 2015. Available from: http://arks.princeton.edu/ark:/88435/dsp01h989r552k

Princeton University

7. Lu, Junwei. Combinatorial Inference for Large-Scale Data Analysis .

Degree: PhD, 2018, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01nk322h04t

► Problems of inferring the combinatorial structures of networks arise in many real applications ranging from genomic regulatory networks, brain networks to social networks. This poses…
(more)

Subjects/Keywords: Combinatorial inference; Hypothesis testing; Property test; Universality

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APA (6^{th} Edition):

Lu, J. (2018). Combinatorial Inference for Large-Scale Data Analysis . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01nk322h04t

Chicago Manual of Style (16^{th} Edition):

Lu, Junwei. “Combinatorial Inference for Large-Scale Data Analysis .” 2018. Doctoral Dissertation, Princeton University. Accessed November 19, 2019. http://arks.princeton.edu/ark:/88435/dsp01nk322h04t.

MLA Handbook (7^{th} Edition):

Lu, Junwei. “Combinatorial Inference for Large-Scale Data Analysis .” 2018. Web. 19 Nov 2019.

Vancouver:

Lu J. Combinatorial Inference for Large-Scale Data Analysis . [Internet] [Doctoral dissertation]. Princeton University; 2018. [cited 2019 Nov 19]. Available from: http://arks.princeton.edu/ark:/88435/dsp01nk322h04t.

Council of Science Editors:

Lu J. Combinatorial Inference for Large-Scale Data Analysis . [Doctoral Dissertation]. Princeton University; 2018. Available from: http://arks.princeton.edu/ark:/88435/dsp01nk322h04t

8. Yao, Jiawei. Factor Models: Testing and Forecasting .

Degree: PhD, 2015, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01ww72bd75b

► This dissertation focuses on two aspects of factor models, testing and forecasting. For testing, we investigate a more general high-dimensional testing problem, with an emphasis…
(more)

Subjects/Keywords: factor models; inverse regression; power enhancement; screening; sparse alternatives; sufficient forecasting

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APA (6^{th} Edition):

Yao, J. (2015). Factor Models: Testing and Forecasting . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01ww72bd75b

Chicago Manual of Style (16^{th} Edition):

Yao, Jiawei. “Factor Models: Testing and Forecasting .” 2015. Doctoral Dissertation, Princeton University. Accessed November 19, 2019. http://arks.princeton.edu/ark:/88435/dsp01ww72bd75b.

MLA Handbook (7^{th} Edition):

Yao, Jiawei. “Factor Models: Testing and Forecasting .” 2015. Web. 19 Nov 2019.

Vancouver:

Yao J. Factor Models: Testing and Forecasting . [Internet] [Doctoral dissertation]. Princeton University; 2015. [cited 2019 Nov 19]. Available from: http://arks.princeton.edu/ark:/88435/dsp01ww72bd75b.

Council of Science Editors:

Yao J. Factor Models: Testing and Forecasting . [Doctoral Dissertation]. Princeton University; 2015. Available from: http://arks.princeton.edu/ark:/88435/dsp01ww72bd75b

9. Furger, Alexander Jonathon. High Frequency Asset Factor Models: Applications to Covariance Estimation and Risk Management .

Degree: PhD, 2015, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01cj82k9610

► We document a striking block-diagonal pattern in the factor model residual covariances of the S&P 500 Equity Index constituents, after sorting the assets by their…
(more)

Subjects/Keywords: covariance; factor model; high-dimensional; High-frequency; risk

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APA (6^{th} Edition):

Furger, A. J. (2015). High Frequency Asset Factor Models: Applications to Covariance Estimation and Risk Management . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01cj82k9610

Chicago Manual of Style (16^{th} Edition):

Furger, Alexander Jonathon. “High Frequency Asset Factor Models: Applications to Covariance Estimation and Risk Management .” 2015. Doctoral Dissertation, Princeton University. Accessed November 19, 2019. http://arks.princeton.edu/ark:/88435/dsp01cj82k9610.

MLA Handbook (7^{th} Edition):

Furger, Alexander Jonathon. “High Frequency Asset Factor Models: Applications to Covariance Estimation and Risk Management .” 2015. Web. 19 Nov 2019.

Vancouver:

Furger AJ. High Frequency Asset Factor Models: Applications to Covariance Estimation and Risk Management . [Internet] [Doctoral dissertation]. Princeton University; 2015. [cited 2019 Nov 19]. Available from: http://arks.princeton.edu/ark:/88435/dsp01cj82k9610.

Council of Science Editors:

Furger AJ. High Frequency Asset Factor Models: Applications to Covariance Estimation and Risk Management . [Doctoral Dissertation]. Princeton University; 2015. Available from: http://arks.princeton.edu/ark:/88435/dsp01cj82k9610

10. Mincheva, Martina Zhelcheva. High-Dimensional Structured Covariance Matrix Estimation with Financial Applications .

Degree: PhD, 2014, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01rv042t21s

► This thesis deals with high dimensional statistical inference, and more speci- cally with uncovering low dimensional structures in high dimensional systems. It focuses on large…
(more)

Subjects/Keywords: covariance; factor models; high dimensional; principal component analysis; sparsity

Record Details Similar Records

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APA (6^{th} Edition):

Mincheva, M. Z. (2014). High-Dimensional Structured Covariance Matrix Estimation with Financial Applications . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01rv042t21s

Chicago Manual of Style (16^{th} Edition):

Mincheva, Martina Zhelcheva. “High-Dimensional Structured Covariance Matrix Estimation with Financial Applications .” 2014. Doctoral Dissertation, Princeton University. Accessed November 19, 2019. http://arks.princeton.edu/ark:/88435/dsp01rv042t21s.

MLA Handbook (7^{th} Edition):

Mincheva, Martina Zhelcheva. “High-Dimensional Structured Covariance Matrix Estimation with Financial Applications .” 2014. Web. 19 Nov 2019.

Vancouver:

Mincheva MZ. High-Dimensional Structured Covariance Matrix Estimation with Financial Applications . [Internet] [Doctoral dissertation]. Princeton University; 2014. [cited 2019 Nov 19]. Available from: http://arks.princeton.edu/ark:/88435/dsp01rv042t21s.

Council of Science Editors:

Mincheva MZ. High-Dimensional Structured Covariance Matrix Estimation with Financial Applications . [Doctoral Dissertation]. Princeton University; 2014. Available from: http://arks.princeton.edu/ark:/88435/dsp01rv042t21s

11. Dai, Wei. Statistical Methods in Finance .

Degree: PhD, 2014, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01h989r3354

► This dissertation focuses on statistical methods in finance, with an emphasis on the theories and applications of factor models. Past studies have generated fruitful results…
(more)

Subjects/Keywords: Factor Models; Financial Econometrics; High-dimensional Statistics

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APA (6^{th} Edition):

Dai, W. (2014). Statistical Methods in Finance . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01h989r3354

Chicago Manual of Style (16^{th} Edition):

Dai, Wei. “Statistical Methods in Finance .” 2014. Doctoral Dissertation, Princeton University. Accessed November 19, 2019. http://arks.princeton.edu/ark:/88435/dsp01h989r3354.

MLA Handbook (7^{th} Edition):

Dai, Wei. “Statistical Methods in Finance .” 2014. Web. 19 Nov 2019.

Vancouver:

Dai W. Statistical Methods in Finance . [Internet] [Doctoral dissertation]. Princeton University; 2014. [cited 2019 Nov 19]. Available from: http://arks.princeton.edu/ark:/88435/dsp01h989r3354.

Council of Science Editors:

Dai W. Statistical Methods in Finance . [Doctoral Dissertation]. Princeton University; 2014. Available from: http://arks.princeton.edu/ark:/88435/dsp01h989r3354

12. Gu, Weijie. Estimating False Discovery Proportion under Covariance Dependence .

Degree: PhD, 2012, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp019019s2505

► Multiple hypothesis testing is a fundamental problem in high dimensional inference, with wide applications in many scientific fields. In genome-wide association studies, tens of thousands…
(more)

Subjects/Keywords: approximate factor model; covariance dependence; false discovery proportion; high dimensionality; multiple hypothesis testing

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APA (6^{th} Edition):

Gu, W. (2012). Estimating False Discovery Proportion under Covariance Dependence . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp019019s2505

Chicago Manual of Style (16^{th} Edition):

Gu, Weijie. “Estimating False Discovery Proportion under Covariance Dependence .” 2012. Doctoral Dissertation, Princeton University. Accessed November 19, 2019. http://arks.princeton.edu/ark:/88435/dsp019019s2505.

MLA Handbook (7^{th} Edition):

Gu, Weijie. “Estimating False Discovery Proportion under Covariance Dependence .” 2012. Web. 19 Nov 2019.

Vancouver:

Gu W. Estimating False Discovery Proportion under Covariance Dependence . [Internet] [Doctoral dissertation]. Princeton University; 2012. [cited 2019 Nov 19]. Available from: http://arks.princeton.edu/ark:/88435/dsp019019s2505.

Council of Science Editors:

Gu W. Estimating False Discovery Proportion under Covariance Dependence . [Doctoral Dissertation]. Princeton University; 2012. Available from: http://arks.princeton.edu/ark:/88435/dsp019019s2505

13. Barut, Ahmet Emre. Variable Selection and Prediction in High Dimensional Models .

Degree: PhD, 2013, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01pz50gw21f

► The aim of this thesis is to develop methods for variable selection and statistical prediction for high dimensional statistical problems. Along with proposing new and…
(more)

Subjects/Keywords: Classification; Fisher Discriminant; Generalized Linear Models; High Dimensional Models; Penalized Estimators; Statistics

Record Details Similar Records

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APA (6^{th} Edition):

Barut, A. E. (2013). Variable Selection and Prediction in High Dimensional Models . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01pz50gw21f

Chicago Manual of Style (16^{th} Edition):

Barut, Ahmet Emre. “Variable Selection and Prediction in High Dimensional Models .” 2013. Doctoral Dissertation, Princeton University. Accessed November 19, 2019. http://arks.princeton.edu/ark:/88435/dsp01pz50gw21f.

MLA Handbook (7^{th} Edition):

Barut, Ahmet Emre. “Variable Selection and Prediction in High Dimensional Models .” 2013. Web. 19 Nov 2019.

Vancouver:

Barut AE. Variable Selection and Prediction in High Dimensional Models . [Internet] [Doctoral dissertation]. Princeton University; 2013. [cited 2019 Nov 19]. Available from: http://arks.princeton.edu/ark:/88435/dsp01pz50gw21f.

Council of Science Editors:

Barut AE. Variable Selection and Prediction in High Dimensional Models . [Doctoral Dissertation]. Princeton University; 2013. Available from: http://arks.princeton.edu/ark:/88435/dsp01pz50gw21f

14. Mehta, Chintan. Rank-based Inference for Independent Component Analysis .

Degree: PhD, 2014, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01vm40xr73s

► The focus of this dissertation is statistical inference for Independent Component Analysis (ICA). ICA is a method for representing the joint distribution of multivariate data…
(more)

Record Details Similar Records

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

APA (6^{th} Edition):

Mehta, C. (2014). Rank-based Inference for Independent Component Analysis . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01vm40xr73s

Chicago Manual of Style (16^{th} Edition):

Mehta, Chintan. “Rank-based Inference for Independent Component Analysis .” 2014. Doctoral Dissertation, Princeton University. Accessed November 19, 2019. http://arks.princeton.edu/ark:/88435/dsp01vm40xr73s.

MLA Handbook (7^{th} Edition):

Mehta, Chintan. “Rank-based Inference for Independent Component Analysis .” 2014. Web. 19 Nov 2019.

Vancouver:

Mehta C. Rank-based Inference for Independent Component Analysis . [Internet] [Doctoral dissertation]. Princeton University; 2014. [cited 2019 Nov 19]. Available from: http://arks.princeton.edu/ark:/88435/dsp01vm40xr73s.

Council of Science Editors:

Mehta C. Rank-based Inference for Independent Component Analysis . [Doctoral Dissertation]. Princeton University; 2014. Available from: http://arks.princeton.edu/ark:/88435/dsp01vm40xr73s

15. Ke, Zheng. Inference on large-scale structures .

Degree: PhD, 2014, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp017p88cj77k

► `Big Data' has driven a new statistics branch `Large-Scale Inference' (LSI). In many LSI problems, due to proximity in geography, time, etc., the data may…
(more)

Subjects/Keywords: clustering; Covariate-Assisted Screening; homogeneity; phase diagram; sparsity; variable selection

Record Details Similar Records

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

APA (6^{th} Edition):

Ke, Z. (2014). Inference on large-scale structures . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp017p88cj77k

Chicago Manual of Style (16^{th} Edition):

Ke, Zheng. “Inference on large-scale structures .” 2014. Doctoral Dissertation, Princeton University. Accessed November 19, 2019. http://arks.princeton.edu/ark:/88435/dsp017p88cj77k.

MLA Handbook (7^{th} Edition):

Ke, Zheng. “Inference on large-scale structures .” 2014. Web. 19 Nov 2019.

Vancouver:

Ke Z. Inference on large-scale structures . [Internet] [Doctoral dissertation]. Princeton University; 2014. [cited 2019 Nov 19]. Available from: http://arks.princeton.edu/ark:/88435/dsp017p88cj77k.

Council of Science Editors:

Ke Z. Inference on large-scale structures . [Doctoral Dissertation]. Princeton University; 2014. Available from: http://arks.princeton.edu/ark:/88435/dsp017p88cj77k

16. Tong, Xin. Learning with Asymmetry, High Dimension and Social Networks .

Degree: PhD, 2012, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01dr26xx42r

► Yes or no is perhaps the most common answer we provide each day. Indeed, binary answers to well structured questions are the building blocks of…
(more)

Subjects/Keywords: High Dimension; Neyman-Pearson; ROAD; Social Network

Record Details Similar Records

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APA (6^{th} Edition):

Tong, X. (2012). Learning with Asymmetry, High Dimension and Social Networks . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01dr26xx42r

Chicago Manual of Style (16^{th} Edition):

Tong, Xin. “Learning with Asymmetry, High Dimension and Social Networks .” 2012. Doctoral Dissertation, Princeton University. Accessed November 19, 2019. http://arks.princeton.edu/ark:/88435/dsp01dr26xx42r.

MLA Handbook (7^{th} Edition):

Tong, Xin. “Learning with Asymmetry, High Dimension and Social Networks .” 2012. Web. 19 Nov 2019.

Vancouver:

Tong X. Learning with Asymmetry, High Dimension and Social Networks . [Internet] [Doctoral dissertation]. Princeton University; 2012. [cited 2019 Nov 19]. Available from: http://arks.princeton.edu/ark:/88435/dsp01dr26xx42r.

Council of Science Editors:

Tong X. Learning with Asymmetry, High Dimension and Social Networks . [Doctoral Dissertation]. Princeton University; 2012. Available from: http://arks.princeton.edu/ark:/88435/dsp01dr26xx42r