# Posts Tagged ‘ mathematics ’

## My Favourite Book

April 15, 2015
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Well, perhaps it's not really my favourite book, but it's certainly right up there with the most heavily thumbed tomes on my office bookshelf.I'm referring to Tables of Integrals, Series and Products, by Gradshteyn and Ryzhik. I picked up a used copy o...

## Optimal Design of Experiments

January 11, 2015
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$Optimal Design of Experiments$

The first colloquium speaker at this semester, professor Wei Zheng from IUPUI, will give a talk on “Universally optimal designs for two interference models“. In this data explosive age, people are easy to get big data set, which renders people difficult to make inferences from such massive data. Since people usually think that with more […]

## Econometricians’ Debt to Alan Turing

December 31, 2014
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The other day, Carol and I went with friends to see the movie, The Imitation Game. I definitely recommend it.I was previously aware of many of Alan Turing's contributions, especially in relation to the Turing Machine, cryptography, computing, and artif...

## Machine Learning Books Suggested by Michael I. Jordan from Berkeley

December 30, 2014
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There has been a Machine Learning (ML) reading list of books in hacker news for a while, where Professor Michael I. Jordan recommend some books to start on ML for people who are going to devote many decades of their lives to the field, and who want to get to the research frontier fairly quickly. […]

## Mathematical Statistics Lesson of the Day – Complete Statistics

$Mathematical Statistics Lesson of the Day – Complete Statistics$

The set-up for today’s post mirrors my earlier Statistics Lesson of the Day on sufficient statistics. Suppose that you collected data in order to estimate a parameter .  Let be the probability density function (PDF)* for . Let be a statistic based on . If implies that then  is said to be complete.  To deconstruct this esoteric […]

## Christian Robert Shows that the Sample Median Cannot Be a Sufficient Statistic

I am grateful to Christian Robert (Xi’an) for commenting on my recent Mathematical Statistics Lessons of the Day on sufficient statistics and minimally sufficient statistics. In one of my earlier posts, he wisely commented that the sample median cannot be a sufficient statistic.  He has supplemented this by writing on his own blog to show that […]

## Mathematical Statistics Lesson of the Day – Minimally Sufficient Statistics

$Mathematical Statistics Lesson of the Day – Minimally Sufficient Statistics$

In using a statistic to estimate a parameter in a probability distribution, it is important to remember that there can be multiple sufficient statistics for the same parameter.  Indeed, the entire data set, , can be a sufficient statistic – it certainly contains all of the information that is needed to estimate the parameter.  However, […]

## Can we try to make an adjustment?

November 14, 2014
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In most of our data science teaching (including our book Practical Data Science with R) we emphasize the deliberately easy problem of “exchangeable prediction.” We define exchangeable prediction as: given a series of observations with two distinguished classes of variables/observations denoted “x”s (denoting control variables, independent variables, experimental variables, or predictor variables) and “y” (denoting … Continue reading Can we try to make an adjustment? → Related posts: Don’t use…

## 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, […]

## Mathematical Statistics Lesson of the Day – Sufficient Statistics

$Mathematical Statistics Lesson of the Day – Sufficient Statistics$

*Update on 2014-11-06: Thanks to Christian Robert’s comment, I have removed the sample median as an example of a sufficient statistic. Suppose that you collected data in order to estimate a parameter .  Let be the probability density function (PDF)* for . Let be a statistic based on .  Let be the PDF for . If the […]