Blog Archives

Factor Analysis vs Principal Component Analysis

November 22, 2014
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Factor Analysis vs Principal Component Analysis

Recently some papers discussed in our journal club  are focused on integrative clustering of multiple omics data sets. I found that they are all originated from factor analysis and make use of the advantage of factor analysis over principal component analysis. Let’s recall the model for factor analysis: where () and , with mean and […]

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EM algorithm revisited

November 20, 2014
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EM algorithm revisited

On this Tuesday, Professor Xuming He presented their recent work on subgroup analysis, which is very interesting and useful in reality. Think about the following very much practical problem (since the drug is expensive or has certain amount of side effect): If you are given the drug response, some baseline covariates which have nothing to […]

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Empirical Likelihood meets Bayesian Analysis

November 18, 2014
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Empirical Likelihood meets Bayesian Analysis

The core idea of Empirical Likelihood (EL) is to use a maximum entropy discrete distribution supported on the data points and constrained by estimating equations related with the parameters of interest. As such, it is a non-parametric approach in the sense that the distribution of the data does not need to be specified, only some of […]

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Multiple Linear Regression Revisited

November 10, 2014
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Multiple Linear Regression Revisited

Last night, I had a discussion about the integrative data analysis (closely related with the discussion of AOAS 2014 paper from Dr Xihong Lin’s group and JASA 2014 paper from Dr. Hongzhe Li’s group) with my friend. If some biologist gave you the genetic variants (e.g. SNP) data and the phenotype (e.g. some trait) data, […]

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p-value vs Bayes

September 30, 2014
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p-value vs Bayes

p-value and Bayes are the two hottest words in Statistics. Actually I still can not get why the debate between frequentist  statistics and Bayesian statistics can last so long. What is the essence arguments behind it? (Any one can help me with this?) In my point of view, they are just two ways for solving […]

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It’s time for job application now!

September 29, 2014
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It’s time for job application now!

I collected the following series on applying for faculty positions in 2011, when I was in my second year PhD. Now it’s my turn to apply for jobs. I will share the following useful materials with all you who want to apply for jobs this year. Applying for Jobs: Application Materials Applying for Jobs : […]

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Useful for referring—9-11-2014

September 12, 2014
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Useful for referring—9-11-2014

Some R Resources for GLMs 失联搜救中的统计数据分析 The gap between data mining and predictive models Data Mining, machine learning and statistics. useR! 2014 is underway with 16 tutorials What is Scalable Machine Learning? rlist:基于list在R中处理非关系型数据 The perfect candidate The Leek group guide to giving talks 38 Seminal Articles Every Data Scientist Should Read Deep Learning – important […]

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Welcome to my Homepage

July 10, 2014
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Welcome to my Homepage

My academic homepage just has been launched. Welcome to visit: Honglang Wang’s Homepage.    

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Useful for referring—2-25-2014

February 25, 2014
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Useful for referring—2-25-2014

Interview with Nick Chamandy, statistician at Google You and Your Research +  video Trustworthy Online Controlled Experiments: Five Puzzling Outcomes Explained A Survival Guide to Starting and Finishing a PhD Six Rules For Wearing Suits For Beginners Why I Created C++ More advice to scientists on blogging Software engineering practices for graduate students Statistics Matter […]

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Useful for referring—2-25-2014

February 25, 2014
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Useful for referring—2-25-2014

Interview with Nick Chamandy, statistician at Google You and Your Research +  video Trustworthy Online Controlled Experiments: Five Puzzling Outcomes Explained A Survival Guide to Starting and Finishing a PhD Six Rules For Wearing Suits For Beginners Why I Created C++ More advice to scientists on blogging Software engineering practices for graduate students Statistics Matter […]

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