Proximal Gradient Descent - CMU Statistics

Chapter 14 Within-Subjects Designs - CMU Statistics

Chapter 14 Within-Subjects Designs ... although often the term repeated measures analysis is used in a narrower sense to indicate the speci c set of analyses discussed

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Chapter 9 Simple Linear Regression

Chapter 9 Simple Linear Regression An analysis appropriate for a quantitative outcome and a single quantitative ex-planatory variable. 9.1 …

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Lecture Notes 9 Asymptotic Theory (Chapter 9)

Lecture Notes 9 Asymptotic Theory (Chapter 9) In these notes we look at the large sample properties of estimators, especially the maxi-mum likelihood estimator.

2 Probability Theory and Classical Statistics

2 Probability Theory and Classical Statistics Statistical inference rests on probability theory, and so an in-depth under-standing of the basics of probability theory is necessary for acquiring a con-

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Ryan Tibshirani Data Mining: 36-462/36-662 January 22 2013

Ryan Tibshirani Data Mining: 36-462/36-662 January 22 2013 Optional reading: ESL 14.10 1. Information retrieval with the web Last time:information retrieval, learned how to compute similarity scores (distances) of documents to a given query string But what if …

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Ryan Tibshirani Data Mining: 36-462/36-662 April 25 2013

Boosting Boosting1 is similar to bagging in that we combine the results of several classi cation trees. However, boosting does something fundamentally di erent, and can work a lot better As usual, we start with training data (x

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Chapter 8 Threats to Your Experiment - CMU Statistics

This chapter discusses possible complaints about internal validity, external validity, construct validity, Type 1 error, and power. We are using \threats" to mean things that will reduce the impact of

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Advanced Data Analysis from an Elementary Point of View

Advanced Data Analysis from an Elementary Point of View Cosma Rohilla Shalizi

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Finding Informative Features - CMU Statistics

Similarly, our uncertainty about the class C, in the absence of any other information, is just the entropy of C: H[C] = X c Pr(C= c)log 2 Pr(C= c) Now suppose we observe the value of the feature X.

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Degrees of Freedom and Model Search - CMU Statistics

Degrees of Freedom and Model Search Ryan J. Tibshirani Abstract Degrees of freedom is a fundamental concept in statistical modeling, as it provides a quan-titative description of the amount of tting performed by a given procedure. But, despite this

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Regression shrinkage and selection via the lasso: a ...

276 R.Tibshirani with x+ indicating the positive part, x+ =x·1.x>0/.This is a convex problem, with βˆ i =yi at λ=0 and culminating in the usual isotonic regression as λ→∞.Along the way it gives nearly monotone approximations. .βi −β i+1/+ is ‘half’ of an l1-penalty on differences, penalizing dips but not increases in the sequence. This procedure allows us to assess the ...

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Gradient Descent - CMU Statistics

Ryan Tibshirani Convex Optimization 10-725. Last time: canonical convex programs Linear program (LP): takes the form min x cTx subject to Dx d Ax= b Quadratic program (QP): like LP, but with quadratic criterion Semide nite program (SDP): like LP, but with matrices Conic program: the most general form of all

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