PPT-Regression with Networks / Ego Networks and Randomization

Brian Aronson Review of ego networks Ego network personal network Ego Focal noderespondent Alter Actors ego has ties with Dyad Pair of individuals Ties Ego D C B Tie types Friends. Di64256erentiating 8706S 8706f Setting the partial derivatives to 0 produces estimating equations for the regression coe64259cients Because these equations are in general nonlinear they require solution by numerical optimization As in a linear model Day 1. Lock, Lock, Lock Morgan, Lock, and Lock. MAA . Minicourse. – Joint Mathematics Meetings. Baltimore, MD. January 2014. The Lock. 5. Team. Dennis. Iowa State. Kari. Harvard/Duke. Eric. UNC/Duke. Rajmohan Rajaraman. Northeastern University, Boston. May 2012. Chennai Network Optimization Workshop. The Randomization Repertoire. 1. Randomization in Network Optimization. Very important toolkit:. Often simple routines yielding the best known approximations to hard optimization problems. Kari Lock Morgan. Department of Statistical Science, Duke University. kari@stat.duke.edu. . with Robin Lock, Patti Frazer Lock, Eric Lock, Dennis Lock. Statistics: Unlocking the Power of Data. Wiley Faculty Network. Cardboard Cut Out. Essential Questions. Who am I?. Who do I want to be?. Challenge:. Create a life size cardboard cut out of your alter ego.. You may use whatever two dimensional materials you would like to create your project.. Ece Akca. Ege University. Heidi A. Wayment & Meliksah Demir. Northern Arizona University. Nebi Sumer. Middle East Technical University. Outline. Introduction. The Quiet Ego. Personality Characteristics. invalid instruments:. Egger regression and Weighted Median Approaches. David Evans. What is the problem?. Mendelian. Randomization (MR) uses genetic variants to test for causal relationships between phenotypic exposures and disease-related outcomes. Day 1. Lock, Lock, Lock, Lock, and Lock. MAA . Minicourse. – Joint Mathematics Meetings. San Diego, CA. January 2013. The Lock. 5. Team. Robin. St. Lawrence. Dennis. Iowa State. Eric. Duke. Kari. ¿. Quién. . es. mi . identidad. . secreta. ?. Mi. Alter-Ego. Se llama _________________. Es. de __________________. Tiene. _______________. años. Le . gusta. ________________, _______________, y _____________. Unique. Ever-changing. Situationally. -determined. Continuous. Invariant. 2) _____ is defined as the unique qualities and distinctive behavior patterns of an individual across time and situations.. Genetic makeup. Day 1. Lock, Lock, Lock, Lock, and Lock. Minicourse. – Joint Mathematics Meetings. Boston, MA. January 2012. WiFi. : . marriotconference. , password: 1134ams. Introductions. :. Name. Institution. Day 2. Lock, Lock, Lock, Lock, and Lock. MAA . Minicourse. - Joint Mathematics Meetings. San Diego, CA. January 2013. Schedule: Day 2. Friday, 1/11, 9:00 – 11:00 am. 5. More on Randomization Tests. Introductory Statistics. Robin H. Lock, Burry Professor of Statistics. St. Lawrence University. Breakout Panel. USCOTS 2011 - Raleigh, NC. Intro Stat (Math 113) at St. Lawrence. 26-29 students per section. Author: Peter . Carr. . Presenter: Ken Ngo - 467632. Outline. 1. American put in Black-Scholes . 2. Randomization of the American put with Exponential distribution . . *on non-dividend-paying stock*.