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- Title: 4
- Type: Notes
- School: Berkeley
- Course: STAT 153
- Term: Fall
Coursehero >> California >> Berkeley >> STAT 153
Path: Berkeley >> STAT >> 153 Fall, 2008
Path: Berkeley >> STAT >> 153 Fall, 2008
Path: Berkeley >> STAT >> 153 Fall, 2008
Path: Berkeley >> STAT >> 153 Fall, 2008
Path: Berkeley >> STAT >> 153 Fall, 2008
Path: Berkeley >> STAT >> 153 Fall, 2008
Path: Berkeley >> STAT >> 153 Fall, 2008
Path: Berkeley >> STAT >> 153 Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 204 Fall, 2008
Path: Berkeley >> STAT >> 206a Fall, 2008
Path: Berkeley >> STAT >> 210b Spring, 2008
Path: Berkeley >> STAT >> 210b Spring, 2008
Path: Berkeley >> STAT >> 210b Spring, 2008
Path: Berkeley >> STAT >> 210b Spring, 2008
Path: Berkeley >> STAT >> 210b Spring, 2008
Path: Berkeley >> STAT >> 210b Spring, 2008
Path: Berkeley >> STAT >> 210b Spring, 2008
Path: Berkeley >> STAT >> 210b Spring, 2008
Path: Berkeley >> STAT >> 210b Spring, 2008
Path: Berkeley >> STAT >> 210b Spring, 2008
Path: Berkeley >> STAT >> 210b Spring, 2008
Path: Berkeley >> STAT >> 210b Spring, 2008
Path: Berkeley >> STAT >> 210b Spring, 2008
Path: Berkeley >> STAT >> 210b Spring, 2008
Path: Berkeley >> STAT >> 210b Spring, 2008
Path: Berkeley >> STAT >> 25 Fall, 2008
Path: Berkeley >> STAT >> 25 Fall, 2008
Path: Berkeley >> STAT >> 25 Fall, 2008
Path: Berkeley >> STAT >> 25 Fall, 2008
Path: Berkeley >> STAT >> 25 Fall, 2008
Path: Berkeley >> STAT >> 25 Fall, 2008
Path: Berkeley >> STAT >> 25 Fall, 2008
Path: Berkeley >> STAT >> 25 Fall, 2008
Path: Berkeley >> STAT >> 25 Fall, 2008
Path: Berkeley >> STAT >> 25 Fall, 2008
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Assignment Stat153 4 (due October 31, 2007) 1. (ARIMA models) Shumway and Sto er problem 3.31. The annual global temperature deviations data for 1880-2004 is available at http://www.stat.pitt.edu/sto er/tsa2/data/globtemp2.dat. is It the second column. 2. (Seasonal ARIMA models) Shumway and Sto er problem 3.35. The monthly unemployment data for 1948-1978 is available at http://www.stat.pitt.edu/sto er/tsa2/data/unemp.dat. 1
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22.pdf
Path: Berkeley >> STAT >> 153 Fall, 2008
Description: Introduction to Time Series Analysis. Lecture 22. 1. Review: The periodogram, the smoothed periodogram. 2. Other smoothed spectral estimators. 3. Consistency. 4. Asymptotic distribution. 1 Review: Periodogram The periodogram is dened as 2 2 I() = X...
1.pdfPath: Berkeley >> STAT >> 153 Fall, 2008
Description: Stat153 Assignment 1 (due September 10, 2007) 1. (Weak versus strict stationarity) This example shows that a stationary process is not necessarily strictly stationary. Suppose that {Wt } and {Zt } are independent and identically distributed (i.i.d.) ...
11.pdfPath: Berkeley >> STAT >> 153 Fall, 2008
Description: Introduction to Time Series Analysis. Lecture 11. Peter Bartlett Last lecture: Forecasting. 1. The innovations representation. 2. Recursive method: Innovations algorithm. 1 Introduction to Time Series Analysis. Lecture 11. 1. Review: Forecasting. 2...
17.pdfPath: Berkeley >> STAT >> 153 Fall, 2008
Description: Introduction to Time Series Analysis. Lecture 17. 1. Spectral density: Facts and examples. 2. Spectral distribution function. 3. Wolds decomposition. 1 A periodic time series Consider Xt = A sin(2t) + B cos(2t) = C sin(2t + ), where A, B are uncorr...
25.pdfPath: Berkeley >> STAT >> 153 Fall, 2008
Description: Introduction to Time Series Analysis. Lecture 25. 1. Lagged regression models. 2. Review: lagged regression in the time domain 3. Cross spectrum. Coherence. 4. Lagged regression in the frequency domain. 1 Lagged regression models Consider a lagged ...
3.pdfPath: Berkeley >> STAT >> 153 Fall, 2008
Description: Stat153 Assignment 3 (due October 10, 2007) 1. (Linear prediction) Suppose that {Xt } is an AR(1) process, we have observed X1 , X3 , and X4 , and we would like to estimate the missing value X2 . Find the best linear predictor of X2 given X1 , X3 , a...
14.pdfPath: Berkeley >> STAT >> 153 Fall, 2008
Description: Introduction to Time Series Analysis. Lecture 14. Last lecture: Yule-Walker estimation 1. Maximum likelihood estimation 2. Large-sample distribution of MLE 1 Parameter estimation: Maximum likelihood estimator One approach: Assume that {Xt } is Gaus...
2.pdfPath: Berkeley >> STAT >> 153 Fall, 2008
Description: Stat153 Assignment 2 (due September 26, 2007) 1. (ACF of AR(1) Shumway and Stoer problem 3.2. 2. (ACF of MA) (a) Find the autocovariance function of the time series Xt = Wt 1.5Wt1 Wt2 , where {Wt } W N (0, 1). (b) Find the autocovariance functi...
24.pdfPath: Berkeley >> STAT >> 153 Fall, 2008
Description: Introduction to Time Series Analysis. Lecture 24. 1. Review: parametric spectral estimation 2. Lagged regression models. 3. Cross-covariance function, sample CCF. 4. Lagged regression in the time domain: prewhitening. 1 Review: Parametric spectral ...
WP_RANDOM.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: Complete Convergence of Message Passing Algorithms for some Satisability Problems Uriel Feige1 , Elchanan Mossel2 and Dan Vilenchik3 1 Micorosoft Research and The Weizmann Institute. urifeige@microsoft.com 2 U.C. Berkeley mossel@stat.berkeley.edu 3 ...
WP_RANDOM.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: Complete Convergence of Message Passing Algorithms for some Satisability Problems Uriel Feige1 , Elchanan Mossel2 and Dan Vilenchik3 1 Micorosoft Research and The Weizmann Institute. urifeige@microsoft.com 2 U.C. Berkeley mossel@stat.berkeley.edu 3 ...
sep28.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: STAT 206A: Gibbs Measures 10 Lecture 10 Lecture date: Sept 28 Scribe: Aleksandr Simma 1 Introduction to Linear Codes For the purposes of coding, we will be working with linear algebra on nite elds. We will n only need to work with the eld F2 , c...
sep28.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: STAT 206A: Gibbs Measures 10 Lecture 10 Lecture date: Sept 28 Scribe: Aleksandr Simma 1 Introduction to Linear Codes For the purposes of coding, we will be working with linear algebra on nite elds. We will n only need to work with the eld F2 , c...
oct12.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: STAT 206A: Gibbs Measures Fall 2006 Lecture 14 Lecture date: Oct 12 Scribe: Alex Fabrikant 1 Capacity of LDPC codes As in the preceding lecture, let us dene a Binary Symmetric Channel (BSC) with parameter p, which, for each bit of the transmissi...
oct12.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: STAT 206A: Gibbs Measures Fall 2006 Lecture 14 Lecture date: Oct 12 Scribe: Alex Fabrikant 1 Capacity of LDPC codes As in the preceding lecture, let us dene a Binary Symmetric Channel (BSC) with parameter p, which, for each bit of the transmissi...
sep12.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: STAT 206A: Gibbs Measures Invited Speaker: Andrea Montanari Lecture 5: Random Energy Model Lecture date: September 12 Scribe: Sebastien Roch This is a guest lecture by Andrea Montanari (ENS Paris and Stanford) on the Random Energy Model (REM) in p...
sep12.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: STAT 206A: Gibbs Measures Invited Speaker: Andrea Montanari Lecture 5: Random Energy Model Lecture date: September 12 Scribe: Sebastien Roch This is a guest lecture by Andrea Montanari (ENS Paris and Stanford) on the Random Energy Model (REM) in p...
sep7.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: STAT 206A: Gibbs Measures 4 Lecture 4 Lecture date: Sep 7 Scribe: Allan Sly 1 SATs Denition 1 A SAT formula has n variables and m constraints or clauses. The variables are denoted {xi }n , xi {0, 1} and we denote xi = 1 xi and zi is a literal ...
sep7.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: STAT 206A: Gibbs Measures 4 Lecture 4 Lecture date: Sep 7 Scribe: Allan Sly 1 SATs Denition 1 A SAT formula has n variables and m constraints or clauses. The variables are denoted {xi }n , xi {0, 1} and we denote xi = 1 xi and zi is a literal ...
de.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: The dierential equation method for random graph processes and greedy algorithms N. C. Wormald Department of Mathematics University of Melbourne Parkville, VIC 3052, Australia Contents 1 Introduction 1.1 A brief look at the general method 1.2 Graph p...
de.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: The dierential equation method for random graph processes and greedy algorithms N. C. Wormald Department of Mathematics University of Melbourne Parkville, VIC 3052, Australia Contents 1 Introduction 1.1 A brief look at the general method 1.2 Graph p...
sep5.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: STAT 206A: Gibbs Measures Elchanan Mossel Lecture 3 Lecture date: Sep 05 Scribe: Alexandre Stauer 1 Ensembles of Factor Graphs An ensemble of factor graphs is a family of randomly chosen factor graphs. In this lectures notes we dene two models o...
sep5.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: STAT 206A: Gibbs Measures Elchanan Mossel Lecture 3 Lecture date: Sep 05 Scribe: Alexandre Stauer 1 Ensembles of Factor Graphs An ensemble of factor graphs is a family of randomly chosen factor graphs. In this lectures notes we dene two models o...
rand_mat.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: Random Vectors of Bounded Weight and Their Linear Dependencies Nathan Linial Dror Weitz December 16, 2000 Abstract Let be a probability distribution on a vector space V . When m vectors u1 , . . . , um are drawn from , how likely are they to ...
rand_mat.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: Random Vectors of Bounded Weight and Their Linear Dependencies Nathan Linial Dror Weitz December 16, 2000 Abstract Let be a probability distribution on a vector space V . When m vectors u1 , . . . , um are drawn from , how likely are they to ...
aug31.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: STAT 206A: Gibbs Measures 2 Lecture 2 Lecture date: Aug 31 Scribe: Omid Etesami Denition 1 Consider a nite set X, and a probability distribution over X n such that for every x X n , m 1 a (xa ), P[x] = Z a=1 where a [n] and a : X |a| R+ . Dene...
aug31.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: STAT 206A: Gibbs Measures 2 Lecture 2 Lecture date: Aug 31 Scribe: Omid Etesami Denition 1 Consider a nite set X, and a probability distribution over X n such that for every x X n , m 1 a (xa ), P[x] = Z a=1 where a [n] and a : X |a| R+ . Dene...
oct26.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: STAT 206A: Gibbs Measures Elchanan Mossel Lecture 18 Lecture date: October 26 Scribe: Partha S. Dey In the previous lecture we dened the loopy belief propagation (LBP) algorithm for a factor graph G as the following iteration of messages. For ever...
oct26.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: STAT 206A: Gibbs Measures Elchanan Mossel Lecture 18 Lecture date: October 26 Scribe: Partha S. Dey In the previous lecture we dened the loopy belief propagation (LBP) algorithm for a factor graph G as the following iteration of messages. For ever...
eraseIT.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 47, NO. 2, FEBRUARY 2001 569 Efficient Erasure Correcting Codes Michael G. Luby, Michael Mitzenmacher, M. Amin Shokrollahi, and Daniel A. Spielman AbstractWe introduce a simple erasure recovery algorith...
eraseIT.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 47, NO. 2, FEBRUARY 2001 569 Efficient Erasure Correcting Codes Michael G. Luby, Michael Mitzenmacher, M. Amin Shokrollahi, and Daniel A. Spielman AbstractWe introduce a simple erasure recovery algorith...
aug29.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: STAT 206A: Gibbs Measures Fall 2006 Lecture 1 Lecture date: Aug 29 Scribe: Madhur Tulsiani This lecture considers a few historical and motivating examples. 1 The Ising Model The Ising model is used to model the spins of atoms in a physical syst...
aug29.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: STAT 206A: Gibbs Measures Fall 2006 Lecture 1 Lecture date: Aug 29 Scribe: Madhur Tulsiani This lecture considers a few historical and motivating examples. 1 The Ising Model The Ising model is used to model the spins of atoms in a physical syst...
oct31.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: STAT 206A: Gibbs Measures Elchanan Mossel Lecture 20 Lecture date: Nov 2 Scribe: Guy Bresler 1 Ising Model on Trees In this lecture we examine when uniqueness holds for the Ising Model. The rst section gives an exact result for trees; the second...
oct31.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: STAT 206A: Gibbs Measures Elchanan Mossel Lecture 20 Lecture date: Nov 2 Scribe: Guy Bresler 1 Ising Model on Trees In this lecture we examine when uniqueness holds for the Ising Model. The rst section gives an exact result for trees; the second...
oct5.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: STAT 206A: Gibbs Measures Elchanan Mossel Lecture 12 Lecture date: Oct 5 Scribe: Guy Bresler In the previous lecture we derived an expression for the expected number of codewords having weight w = N for a general LDPCN (, P ) code: F W (w) = E=0...
oct5.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: STAT 206A: Gibbs Measures Elchanan Mossel Lecture 12 Lecture date: Oct 5 Scribe: Guy Bresler In the previous lecture we derived an expression for the expected number of codewords having weight w = N for a general LDPCN (, P ) code: F W (w) = E=0...
sep26.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: STAT 206A: Gibbs Measures 9 Lecture 9 Lecture date: SEP 26 Scribe: Jian Ding 1 Brief Introduction to Second Moment Method In previous lecture, we presented a lower bound for threshold of satisability problem by unit clause propagation algorithm....
sep26.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: STAT 206A: Gibbs Measures 9 Lecture 9 Lecture date: SEP 26 Scribe: Jian Ding 1 Brief Introduction to Second Moment Method In previous lecture, we presented a lower bound for threshold of satisability problem by unit clause propagation algorithm....
oct19.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: STAT 206A: Gibbs Measures Fall 2006 Lecture 16 Lecture date: Oct. 19 Scribe: Moorea Brega Recall that a distribution tree factorizes according to some factor graph ([N ], [M ], {a : 1 a M }), 1 P (x) = Z M a (xa ). a=1 Denition 1 A family of f...
oct19.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: STAT 206A: Gibbs Measures Fall 2006 Lecture 16 Lecture date: Oct. 19 Scribe: Moorea Brega Recall that a distribution tree factorizes according to some factor graph ([N ], [M ], {a : 1 a M }), 1 P (x) = Z M a (xa ). a=1 Denition 1 A family of f...
oct24.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: STAT 206A: Gibbs Measures Fall 2006 Lecture 17 Lecture date: Oct 24 Scribe: Joel Meord Denition 1 (-expander) A graph G = (V, E) is a -expander if, subsets S V, |S| |V | we have |S| |S| 2 Example 2 (Ising model) Consider the Ising model, 1 whe...
oct24.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: STAT 206A: Gibbs Measures Fall 2006 Lecture 17 Lecture date: Oct 24 Scribe: Joel Meord Denition 1 (-expander) A graph G = (V, E) is a -expander if, subsets S V, |S| |V | we have |S| |S| 2 Example 2 (Ising model) Consider the Ising model, 1 whe...
sep14.pdfPath: Berkeley >> STAT >> 204 Fall, 2008
Description: STAT 206A: Gibbs Measures Elchanan Mossel Lecture 6: Random k-SAT problems Lecture date: Sept 14 Scribe: Guillaume Obozinski Denition 1 (SATN (k, ) is a random k-SAT formula on N variables where each N possible clause is chosen independently with...
sep14.pdfPath: Berkeley >> STAT >> 206a Fall, 2008
Description: STAT 206A: Gibbs Measures Elchanan Mossel Lecture 6: Random k-SAT problems Lecture date: Sept 14 Scribe: Guillaume Obozinski Denition 1 (SATN (k, ) is a random k-SAT formula on N variables where each N possible clause is chosen independently with...
caus2004syl.pdfPath: Berkeley >> STAT >> 210b Spring, 2008
Description: Course:Censored Longitudinal Data and Causality Course No: PUBLIC HEALTH 246A sec 1 (Lecture), PUBLIC HEALTH 298 sec. 50 (Lab) Instructors: Mark van der Laan and Alan Hubbard Lecture Time and Place: M-W 12-1:30 in Tolman 235 Lab Time and Place: F 3-5...
Syllabus.docPath: Berkeley >> STAT >> 210b Spring, 2008
Description: Statistical Methods for Biomarker Discovery BASS 2007 Workshop, November 7th-9th Mark van der Laan, Jiann-Ping Hsu/Karl E. Peace Professor in Biostatistics, UC Berkeley laan@berkeley.edu Cathy Tuglus Graduate Student in Biostatistics, UC Berkeley ctu...
BASS_sec4_1_twoStageMTP.pptPath: Berkeley >> STAT >> 210b Spring, 2008
Description: Controlling FDR in Second Stage Analysis Catherine Tuglus Work with Mark van der Laan UC Berkeley Biostatistics Outline What is a Second Stage Analysis Issues with MTP for Secondary Analysis Proposed solution for Marginal FDR controlling pro...
compbio.pdfPath: Berkeley >> STAT >> 210b Spring, 2008
Description: Chapter 1 Introduction 1.1 Common terms in molecular biology. DNA, RNA DNA is a polymer of four possible nucleotides denoted with A (adenine), C (cytosine), T (thymine), G (guanine). A nucleotide is a molecule connecting a phosphate group to a ve c...
BASS_sec3_1.pptPath: Berkeley >> STAT >> 210b Spring, 2008
Description: Multiple Testing Mark J. van der Laan Division of Biostatistics U.C. Berkeley www.stat.berkeley.edu/~laan Outline Multiple Testing for variable importance in prediction Overview of Multiple Testing Previous proposals of joint null distribution i...
compbiosyl2001.pdfPath: Berkeley >> STAT >> 210b Spring, 2008
Description: PH 243A STATISTICAL TECHNIQUES FOR GENE EXPRESSION DATA, Fall 2001 ROUGH SYLLABUS Lecture 1, Wednesday, September 5: SOME MICROBIOLOGY CROARRAY TECHNOLOGY, EXAMPLES OF DATA SETS. TERMS, THE MI- Possible books for the biology and biotechnology: 1) Da...
BASS_sec1_1.pptPath: Berkeley >> STAT >> 210b Spring, 2008
Description: STATISTICAL LEARNING FROM DATA LIES, DAMNED LIES, AND STATISTICS, Mark Twain. Senate Approves Tighter Policing of Drug Makers, May 8, 2007 Mark van der Laan, www.stat.berkeley.edu/~laan OVERVIEW How good is the human statistical intuition? Statist...
causal2004.pdfPath: Berkeley >> STAT >> 210b Spring, 2008
Description: 8/30/2004 NOTES Logistics Logistics, oce hour times, the course website, relevant texts, and a list of topics to be covered are given on the course syllabus. This syllabus can be obtained from Mark van der Laan or Alan Hubbard. Course evaluation wil...
dsaslides.pdfPath: Berkeley >> STAT >> 210b Spring, 2008
Description: Data Adaptive Estimation of the Treatment Specic Mean in Causal Inference R-package cvDSA Yue Wang Division of Biostatistics Nov. 2004 Nov. 8, 2004 1 Outlines Introduction: Data structure and Marginal Structural Model. Estimation Road map Choice...
BASS_sec3_3_Intro.pptPath: Berkeley >> STAT >> 210b Spring, 2008
Description: Multiple Testing Procedures Examples and Software Implementation Multiple Testing in Action Examples From New Book Multiple Testing Procedures with Applications to Genomics (2007). S. Dudoit and M. J. van der Laan. Multiple Testing Software R packa...
BASS_sec2_tMLE.pptPath: Berkeley >> STAT >> 210b Spring, 2008
Description: Targeted MLE for Variable Importance and Causal Effect with Clinical Trial and Observational Data Mark van der Laan works.bepress.com/mark_van_der_laan Division of Biostatistics, University of California, Berkeley Outline Standard approaches...
fall2003.pdfPath: Berkeley >> STAT >> 210b Spring, 2008
Description: Multivariate Statistical Methods in Genomics PH 243 A, MW 12-2, 2305 Tolman Instructor: Mark van der Laan Oce: Haviland Hall 108, tel: 643-9866 website: www.stat.berkeley.edu/ laan Technical reports at www.bepress.com/ucbbiostat/ email: laan@stat.ber...
BASS_sec1_3.1.pdfPath: Berkeley >> STAT >> 210b Spring, 2008
Description: Super Learning Eric Polley Joint work with Mark van der Laan and Alan Hubbard e-mail: ecpolley@berkeley.edu PH 246C (Berkeley) Super Learning PH 246C 1 / 27 Outline 1 Cross-Validation Denitions Oracle Inequalities 2 Super Learning Denitions...
BASS_sec1_3.2.pptPath: Berkeley >> STAT >> 210b Spring, 2008
Description: Super Learning in Prediction HIV Example Mark van der Laan www.bepress.com/ucbbiostat Division of Biostatistics, University of California, Berkeley Outline Super Learning in Prediction of HIV Phenotype based on HIV Genotype Scientific Goal Predic...
stat210b.pdfPath: Berkeley >> STAT >> 210b Spring, 2008
Description: Empirical Processes - Introduction April 8, 2005 Notation and Basic Setup We will assume that O1 , ., On P i.i.d. Pn is the empirical distribution. P f means Ep f (O) = f dP , likewise Pn f = f dPn . F will represent a set of real-valued functions...
Lecture14.pdfPath: Berkeley >> STAT >> 25 Fall, 2008
Description: STAT C206A / MATH C223A : Curie-Weiss and KMT Strong Embedding 1 Lecture 14 Lecture date: Sept 28, 2007 Scribe: John Zhu 1 Some nal remarks about the Curie-Weiss concentration 1 n In the Curie Weiss model, if m() = i , then +t n exp nt2 . 4(...
quiz-4-sol.pdfPath: Berkeley >> STAT >> 25 Fall, 2008
Description: Stat 25, Quiz 4 Solutions May 9, 2006 1. (a) If H0 is rejected, it means that the data is inconsistent with H0 . Thus the scale is (almost certainly) out of calibration, so the answer is (ii). (b) If H0 is not rejected, it means that the data is co...
gtlect.pdfPath: Berkeley >> STAT >> 25 Fall, 2008
Description: Game Theory, Alive Yuval Peres i The author would like to cordially thank Alan Hammond, for scribing the rst draft; Gabor Pete, Yun Long and Peter Ralph for scribing the revisions; Yelena Shvets for pictures and editing; Ranjit Samra of rojaysorigi...
stat155fall08-practice.pdfPath: Berkeley >> STAT >> 25 Fall, 2008
Description: Stat 155 Fall 08 Practice Final December 9, 2008 Time: 3 hours. Please show all steps. 1. Consider a game of nim with inital conguration (48, 23, 74, 10) and with the restriction that at most three chips can be removed from a pile in a single move....
Lecture18.pdfPath: Berkeley >> STAT >> 25 Fall, 2008
Description: STAT C206A / MATH C223A : Steins method and applications 1 Lecture 18 Lecture date: Oct 8, 2007 Scribe: Guy Bresler 1 Tusndys Lemma a Lemma 1 (Tusndys Lemma, Lemma 3.10 in handout) Let 1 , . . . , n be i.i.d. a symmetric 1 random variables and S...
stat134hw3.pdfPath: Berkeley >> STAT >> 25 Fall, 2008
Description: Stat 134 Section 2 Homework 3 (Due: Wednesday, 9/27) Problems from Pitmans book: Section 2.1: 1, 5, 7. Section 3.2: 3, 6, 13. 1 ...
hw-2-sol.pdfPath: Berkeley >> STAT >> 25 Fall, 2008
Description: Stat 25, Homework # 2 Solution February 25, 2006 1. n!. If n = 10, then this is 3628800. 2. 310 . 3. (a) S = {(1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (2, 1), (2, 2), (2, 3), (2, 4), (2, 5), (2, 6), (3, 1), (3, 2), (3, 3), (3, 4), (3, 5), (3...
talk_normal.pdfPath: Berkeley >> STAT >> 25 Fall, 2008
Description: A new method of normal approximation Sourav Chatterjee (UCB) Sourav Chatterjee A new method of normal approximation Central limit theorems Classical CLT: If X1 , . . . , Xn are independent random variables with zero mean and nite variance and (.),...
Lecture23.pdfPath: Berkeley >> STAT >> 25 Fall, 2008
Description: STAT C206A / MATH C223A : Steins method and applications Fall 2007 Lecture 23 Lecture date: Oct. 19, 2007 Scribe: Partha Dey Recall that, in the Sherrington Kirkpatrick model, the probability of a conguration = (i )N {1, +1}N is i=1 P() = 1 ZN e...
hw-3-sol.pdfPath: Berkeley >> STAT >> 25 Fall, 2008
Description: Stat 25, Homework # 3 Solution February 25, 2006 1. P (A B c ) = P (A) + P (B c ) P (A B c ) = P (A) + P (B c ) P (A)P (B c ) = P (A) + (1 P (B) P (A)(1 P (B) = 1 P (B) + P (A)P (B). 2. Let R1 be the event that engine 1 is running. Let R2 b...