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You searched for +publisher:"Michigan Technological University" +contributor:("Qiuying Sha"). Showing records 1 – 8 of 8 total matches.

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Michigan Technological University

1. Li, Xueling. STATISTICAL METHODS FOR JOINT ANALYSIS OF MULTIPLE PHENOTYPES AND THEIR APPLICATIONS FOR PHEWAS.

Degree: PhD, Department of Mathematical Sciences, 2019, Michigan Technological University

  Genome-wide association studies (GWAS) have successfully detected tens of thousands of robust SNP-trait associations. Earlier researches have primarily focused on association studies of genetic… (more)

Subjects/Keywords: Statistical Methodology Development; UK Biobank; PheWAS; Joint Analysis of Multiple Phenotypes; Applied Statistics; Bioinformatics; Biostatistics; Health Information Technology; Respiratory Tract Diseases; Statistical Methodology; Statistics and Probability

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

APA (6th Edition):

Li, X. (2019). STATISTICAL METHODS FOR JOINT ANALYSIS OF MULTIPLE PHENOTYPES AND THEIR APPLICATIONS FOR PHEWAS. (Doctoral Dissertation). Michigan Technological University. Retrieved from https://digitalcommons.mtu.edu/etdr/813

Chicago Manual of Style (16th Edition):

Li, Xueling. “STATISTICAL METHODS FOR JOINT ANALYSIS OF MULTIPLE PHENOTYPES AND THEIR APPLICATIONS FOR PHEWAS.” 2019. Doctoral Dissertation, Michigan Technological University. Accessed April 13, 2021. https://digitalcommons.mtu.edu/etdr/813.

MLA Handbook (7th Edition):

Li, Xueling. “STATISTICAL METHODS FOR JOINT ANALYSIS OF MULTIPLE PHENOTYPES AND THEIR APPLICATIONS FOR PHEWAS.” 2019. Web. 13 Apr 2021.

Vancouver:

Li X. STATISTICAL METHODS FOR JOINT ANALYSIS OF MULTIPLE PHENOTYPES AND THEIR APPLICATIONS FOR PHEWAS. [Internet] [Doctoral dissertation]. Michigan Technological University; 2019. [cited 2021 Apr 13]. Available from: https://digitalcommons.mtu.edu/etdr/813.

Council of Science Editors:

Li X. STATISTICAL METHODS FOR JOINT ANALYSIS OF MULTIPLE PHENOTYPES AND THEIR APPLICATIONS FOR PHEWAS. [Doctoral Dissertation]. Michigan Technological University; 2019. Available from: https://digitalcommons.mtu.edu/etdr/813


Michigan Technological University

2. Perera, Sachithra. LITERATURE REVIEW FOR PREDICTING 30-DAY HOSPITAL READMISSION.

Degree: MS, Department of Mathematical Sciences, 2018, Michigan Technological University

  The hospital readmission of patients within 30-days of discharge is crucial to healthcare quality and has involved with high costs in Medicare expenditures. Reducing… (more)

Subjects/Keywords: hospital readmission; predict risk; machine learning; social factors; CMS; penalty; Statistics and Probability

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APA (6th Edition):

Perera, S. (2018). LITERATURE REVIEW FOR PREDICTING 30-DAY HOSPITAL READMISSION. (Masters Thesis). Michigan Technological University. Retrieved from https://digitalcommons.mtu.edu/etdr/676

Chicago Manual of Style (16th Edition):

Perera, Sachithra. “LITERATURE REVIEW FOR PREDICTING 30-DAY HOSPITAL READMISSION.” 2018. Masters Thesis, Michigan Technological University. Accessed April 13, 2021. https://digitalcommons.mtu.edu/etdr/676.

MLA Handbook (7th Edition):

Perera, Sachithra. “LITERATURE REVIEW FOR PREDICTING 30-DAY HOSPITAL READMISSION.” 2018. Web. 13 Apr 2021.

Vancouver:

Perera S. LITERATURE REVIEW FOR PREDICTING 30-DAY HOSPITAL READMISSION. [Internet] [Masters thesis]. Michigan Technological University; 2018. [cited 2021 Apr 13]. Available from: https://digitalcommons.mtu.edu/etdr/676.

Council of Science Editors:

Perera S. LITERATURE REVIEW FOR PREDICTING 30-DAY HOSPITAL READMISSION. [Masters Thesis]. Michigan Technological University; 2018. Available from: https://digitalcommons.mtu.edu/etdr/676


Michigan Technological University

3. Yang, Xinlan. STATISTICAL METHODS FOR DETECTING CAUSAL RARE VARIANTS AND ANALYZING MULTIPLE PHENOTYPES.

Degree: PhD, Department of Mathematical Sciences, 2018, Michigan Technological University

  This dissertation includes two papers with each distributed in one chapter. To date, genome-wide association studies (GWAS) have identified a large number of common… (more)

Subjects/Keywords: association test; rare variants; multiple phenotypes; cross-validation; ridge regression; Biostatistics

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APA (6th Edition):

Yang, X. (2018). STATISTICAL METHODS FOR DETECTING CAUSAL RARE VARIANTS AND ANALYZING MULTIPLE PHENOTYPES. (Doctoral Dissertation). Michigan Technological University. Retrieved from https://digitalcommons.mtu.edu/etdr/634

Chicago Manual of Style (16th Edition):

Yang, Xinlan. “STATISTICAL METHODS FOR DETECTING CAUSAL RARE VARIANTS AND ANALYZING MULTIPLE PHENOTYPES.” 2018. Doctoral Dissertation, Michigan Technological University. Accessed April 13, 2021. https://digitalcommons.mtu.edu/etdr/634.

MLA Handbook (7th Edition):

Yang, Xinlan. “STATISTICAL METHODS FOR DETECTING CAUSAL RARE VARIANTS AND ANALYZING MULTIPLE PHENOTYPES.” 2018. Web. 13 Apr 2021.

Vancouver:

Yang X. STATISTICAL METHODS FOR DETECTING CAUSAL RARE VARIANTS AND ANALYZING MULTIPLE PHENOTYPES. [Internet] [Doctoral dissertation]. Michigan Technological University; 2018. [cited 2021 Apr 13]. Available from: https://digitalcommons.mtu.edu/etdr/634.

Council of Science Editors:

Yang X. STATISTICAL METHODS FOR DETECTING CAUSAL RARE VARIANTS AND ANALYZING MULTIPLE PHENOTYPES. [Doctoral Dissertation]. Michigan Technological University; 2018. Available from: https://digitalcommons.mtu.edu/etdr/634


Michigan Technological University

4. Yosof, Fadhila. CUR Matrix Decompositions Method for Joint Analysis of Multiple Phenotypes.

Degree: MS, Department of Mathematical Sciences, 2018, Michigan Technological University

  There are many methods that can be applied to reduce the dimension of large data sets such as principal components analysis (PCA). Many of… (more)

Subjects/Keywords: CUR DECOMPOSITION; Statistical leverage; multiple phenotype; Biostatistics; Statistics and Probability

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APA (6th Edition):

Yosof, F. (2018). CUR Matrix Decompositions Method for Joint Analysis of Multiple Phenotypes. (Masters Thesis). Michigan Technological University. Retrieved from https://digitalcommons.mtu.edu/etdr/635

Chicago Manual of Style (16th Edition):

Yosof, Fadhila. “CUR Matrix Decompositions Method for Joint Analysis of Multiple Phenotypes.” 2018. Masters Thesis, Michigan Technological University. Accessed April 13, 2021. https://digitalcommons.mtu.edu/etdr/635.

MLA Handbook (7th Edition):

Yosof, Fadhila. “CUR Matrix Decompositions Method for Joint Analysis of Multiple Phenotypes.” 2018. Web. 13 Apr 2021.

Vancouver:

Yosof F. CUR Matrix Decompositions Method for Joint Analysis of Multiple Phenotypes. [Internet] [Masters thesis]. Michigan Technological University; 2018. [cited 2021 Apr 13]. Available from: https://digitalcommons.mtu.edu/etdr/635.

Council of Science Editors:

Yosof F. CUR Matrix Decompositions Method for Joint Analysis of Multiple Phenotypes. [Masters Thesis]. Michigan Technological University; 2018. Available from: https://digitalcommons.mtu.edu/etdr/635


Michigan Technological University

5. Fang, Shurong. STATISTICAL METHODS FOR RARE AND COMMON VARIANT ASSOCIATION STUDIES.

Degree: PhD, Department of Mathematical Sciences, 2013, Michigan Technological University

  Complex human diseases are a major challenge for biological research. The goal of my research is to develop effective methods for biostatistics in order… (more)

Subjects/Keywords: Biostatistics; Statistics and Probability

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APA (6th Edition):

Fang, S. (2013). STATISTICAL METHODS FOR RARE AND COMMON VARIANT ASSOCIATION STUDIES. (Doctoral Dissertation). Michigan Technological University. Retrieved from https://digitalcommons.mtu.edu/etd-restricted/103

Chicago Manual of Style (16th Edition):

Fang, Shurong. “STATISTICAL METHODS FOR RARE AND COMMON VARIANT ASSOCIATION STUDIES.” 2013. Doctoral Dissertation, Michigan Technological University. Accessed April 13, 2021. https://digitalcommons.mtu.edu/etd-restricted/103.

MLA Handbook (7th Edition):

Fang, Shurong. “STATISTICAL METHODS FOR RARE AND COMMON VARIANT ASSOCIATION STUDIES.” 2013. Web. 13 Apr 2021.

Vancouver:

Fang S. STATISTICAL METHODS FOR RARE AND COMMON VARIANT ASSOCIATION STUDIES. [Internet] [Doctoral dissertation]. Michigan Technological University; 2013. [cited 2021 Apr 13]. Available from: https://digitalcommons.mtu.edu/etd-restricted/103.

Council of Science Editors:

Fang S. STATISTICAL METHODS FOR RARE AND COMMON VARIANT ASSOCIATION STUDIES. [Doctoral Dissertation]. Michigan Technological University; 2013. Available from: https://digitalcommons.mtu.edu/etd-restricted/103


Michigan Technological University

6. Wang, Zhenchuan. Joint Analysis for Multiple Traits.

Degree: PhD, Department of Mathematical Sciences, 2018, Michigan Technological University

  This dissertation includes three papers with each distributed in one chapter. In chapter 1, we proposed an Adaptive Weighting Reverse Regression (AWRR) method to… (more)

Subjects/Keywords: GWAS; Multiple Traits; Statistics and Probability

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APA (6th Edition):

Wang, Z. (2018). Joint Analysis for Multiple Traits. (Doctoral Dissertation). Michigan Technological University. Retrieved from https://digitalcommons.mtu.edu/etdr/632

Chicago Manual of Style (16th Edition):

Wang, Zhenchuan. “Joint Analysis for Multiple Traits.” 2018. Doctoral Dissertation, Michigan Technological University. Accessed April 13, 2021. https://digitalcommons.mtu.edu/etdr/632.

MLA Handbook (7th Edition):

Wang, Zhenchuan. “Joint Analysis for Multiple Traits.” 2018. Web. 13 Apr 2021.

Vancouver:

Wang Z. Joint Analysis for Multiple Traits. [Internet] [Doctoral dissertation]. Michigan Technological University; 2018. [cited 2021 Apr 13]. Available from: https://digitalcommons.mtu.edu/etdr/632.

Council of Science Editors:

Wang Z. Joint Analysis for Multiple Traits. [Doctoral Dissertation]. Michigan Technological University; 2018. Available from: https://digitalcommons.mtu.edu/etdr/632


Michigan Technological University

7. Liang, Xiaoyu. JOINT ANALYSIS OF MULTIPLE PHENOTYPES IN ASSOCIATION STUDIES.

Degree: PhD, Department of Mathematical Sciences, 2018, Michigan Technological University

  Genome-wide association studies (GWAS) have become a very effective research tool to identify genetic variants of underlying various complex diseases. In spite of the… (more)

Subjects/Keywords: Association Study; Multiple Phenotypes; Adaptive Fisher’s Combination Method; Allele-based Clustering Approach; Hierarchical Clustering Method; Applied Statistics; Biostatistics; Statistical Methodology

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

APA (6th Edition):

Liang, X. (2018). JOINT ANALYSIS OF MULTIPLE PHENOTYPES IN ASSOCIATION STUDIES. (Doctoral Dissertation). Michigan Technological University. Retrieved from https://digitalcommons.mtu.edu/etdr/602

Chicago Manual of Style (16th Edition):

Liang, Xiaoyu. “JOINT ANALYSIS OF MULTIPLE PHENOTYPES IN ASSOCIATION STUDIES.” 2018. Doctoral Dissertation, Michigan Technological University. Accessed April 13, 2021. https://digitalcommons.mtu.edu/etdr/602.

MLA Handbook (7th Edition):

Liang, Xiaoyu. “JOINT ANALYSIS OF MULTIPLE PHENOTYPES IN ASSOCIATION STUDIES.” 2018. Web. 13 Apr 2021.

Vancouver:

Liang X. JOINT ANALYSIS OF MULTIPLE PHENOTYPES IN ASSOCIATION STUDIES. [Internet] [Doctoral dissertation]. Michigan Technological University; 2018. [cited 2021 Apr 13]. Available from: https://digitalcommons.mtu.edu/etdr/602.

Council of Science Editors:

Liang X. JOINT ANALYSIS OF MULTIPLE PHENOTYPES IN ASSOCIATION STUDIES. [Doctoral Dissertation]. Michigan Technological University; 2018. Available from: https://digitalcommons.mtu.edu/etdr/602


Michigan Technological University

8. Zhu, Huanhuan. Statistical Methods for Analyzing Multivariate Phenotypes and Detecting Rare Variant Associations.

Degree: PhD, Department of Mathematical Sciences, 2018, Michigan Technological University

  This dissertation includes four papers with each distributed in one chapter. In chapter 1, I compared the performance of eight multivariate phenotype association tests.… (more)

Subjects/Keywords: Genome-wide association studies; multivariate phenotype association analysis; Phenome-wide association studies; Gene-based rare variant association analysis; Family-based designs; Biostatistics; Statistical Methodology; Statistical Models

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

APA (6th Edition):

Zhu, H. (2018). Statistical Methods for Analyzing Multivariate Phenotypes and Detecting Rare Variant Associations. (Doctoral Dissertation). Michigan Technological University. Retrieved from https://digitalcommons.mtu.edu/etdr/638

Chicago Manual of Style (16th Edition):

Zhu, Huanhuan. “Statistical Methods for Analyzing Multivariate Phenotypes and Detecting Rare Variant Associations.” 2018. Doctoral Dissertation, Michigan Technological University. Accessed April 13, 2021. https://digitalcommons.mtu.edu/etdr/638.

MLA Handbook (7th Edition):

Zhu, Huanhuan. “Statistical Methods for Analyzing Multivariate Phenotypes and Detecting Rare Variant Associations.” 2018. Web. 13 Apr 2021.

Vancouver:

Zhu H. Statistical Methods for Analyzing Multivariate Phenotypes and Detecting Rare Variant Associations. [Internet] [Doctoral dissertation]. Michigan Technological University; 2018. [cited 2021 Apr 13]. Available from: https://digitalcommons.mtu.edu/etdr/638.

Council of Science Editors:

Zhu H. Statistical Methods for Analyzing Multivariate Phenotypes and Detecting Rare Variant Associations. [Doctoral Dissertation]. Michigan Technological University; 2018. Available from: https://digitalcommons.mtu.edu/etdr/638

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