Junshu bao

Junshu Bao in Fall junshu bao of Some pdf files come with Rmd R Markdown source code files while others do not. The Rmd files are added as supplementary material.

Overall Quality Based on 50 ratings. Junshu Bao. Professor in the Statistics department at University of Pittsburgh. Rate Compare. I'm Professor Bao. Professor Bao's Top Tags. Check out Similar Professors in the Statistics Department 5.

Junshu bao

Distortion product otoacoustic emissions DPOAE testing is a promising alternative to behavioral hearing tests and auditory brainstem response testing of pediatric cancer patients. This is accomplished through the construction of normal reference charts, or credible regions, that DP-gram differences lie in, as well as contour probabilities that measure how abnormal or in a certain sense rare a test-retest difference is. A challenge is that the data were collected over varying frequencies, at different time points from baseline, and on possibly one or both ears. A hierarchical structural equation Gaussian process model is proposed to handle the different sources of correlation in the emissions measurements, wherein both subject-specific random effects and variance components governing the smoothness and variability of each child's Gaussian process are coupled together. Keywords: Contour probability; Functional analysis; Growth curves; Serial monitoring; Structural equation modeling. Abstract Distortion product otoacoustic emissions DPOAE testing is a promising alternative to behavioral hearing tests and auditory brainstem response testing of pediatric cancer patients.

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Worked with fellow peers at University of Pittsburgh under supervision of Dr. The purpose of this project was to do a Differential Gene Expression Analysis on a Dexamethasone Treatment data set and incorporate Machine Learning to predict which genes could be differentially expressed. We specifically used files GSM Dexamethasone specifically affects cell growth and can cause apoptosis cell death as specified here. Final PDF file outputted from knitting the source code. Contains all goals, steps, explanations of procedure for this research. Skip to content.

Overall Quality Based on 50 ratings. Junshu Bao. Professor in the Statistics department at University of Pittsburgh. Rate Compare. I'm Professor Bao. Professor Bao's Top Tags.

Junshu bao

Department of Statistics SC. Recent Ph. Challenge the conventional. Create the exceptional. No Limits. Statistical methods for analyzing dependence structures with applications in single-cell experiments. Statistical methods for analyzing multi-omics data: Dependence structure and missing values. Deep learning, clustering, and decision process approaches for modeling time series data. Using concurrent functional regression to reconstruct river stage data during flood events and identify influential functional measurements. Statistical analysis of a joint stochastic model for recurrent competing risk, longitudinal marker, and health status.

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The purpose of these is to peak an audience's interest all while empowering the message behind the data. Her quizzes also felt a lot more difficult than the exams. You will definitely pass the class, but you may fall asleep. Overall Quality Based on 50 ratings. Skip to content. While kind and easy portraying information was not her strong suit. She does everything she can to ensure that you pass. The exams are very fair, open note, and she gives a ton of partial credit. Report repository. And there were two projects that were very easy. You are expected to just know how to do many things on homework. I wish that her grading criteria or just overall instructions for the final project were more clear because that was very very vague. Had Queen Junshu and loved it. View all files.

Statistics knowledge at the level of STAT or above. No prior knowledge of programming required.

Lots of homework Amazing lectures Caring. Dec 6th, She isn't the best lecturer, but she does post the notes which really helps when going back to understand the content. Releases No releases published. The hws are hard and time consuming, but they are sometimes in the textbook, and she is very available outside of class. You are expected to just know how to do many things on homework. Last commit date. Her lectures do not prepare well for the exams. Dismiss alert. Starting from reasonably easy difficulty and ending with hard, this folder utilizes many different data sets to create meaningful, usable data from very messy origins. Take another class. Worked with fellow peers at University of Pittsburgh under supervision of Dr. Amazing lectures Clear grading criteria Caring.