David Izrael

16 papers and 2 presentations; 17 titles; 12 coauthors.

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Conference ×	Year ×	Title ×	Co-authors	Extras	Pages
MWSUG	2018	How to Navigate in a Maze of the Raking Macro with Advanced Weight Trimming	Michael P. Battaglia, Sarah Ball		13
SGF	2017	You Do Not Have to Step On the Same Rake: SAS RAKING Macro, Fourth Generation [presentation]	Michael P. Battaglia, Ann Battaglia, Sarah Ball	source	7
SGF	2016	The Roads We Take Let's Hash It Out! [presentation]	Elizabeth Axelrod	source	7
SESUG	2015	Hash: Is it Always the Best Solution?	Elizabeth Axelrod	source	12
MWSUG	2014	Universal SAS Macro to Parse Any Schema of Ryan White Services (RSR) XML Data	Michael Costa, Fizza S. Gillani		6
NESUG	2013	Constructing a Table of Survey Data with Percent and Confidence Interval in every Direction	Sarah Ball, Sara Donahue		7
SGF	2013	Weighted Sequential Hot Deck Imputation: SAS Macro vs. the SUDAAN PROC HOTDECK	Michael P. Battaglia	source pdf	8
SGF	2013	Working with a Large Pharmacy Database: Hash and Conquer		source pdf	7
NESUG	2012	Working with a Large Pharmacy Database: Hash and Conquer			7
NESUG	2011	Weighted Sehuential Hot Deck Imputation: SAS Macro vs. SUDAANes Proc Hotdeck	Michael P. Battaglia		7
SGF	2009	Extreme Survey Weight Adjustment as a Component of Sample Balancing (a.k.a. Raking)	Michael P. Battaglia, Martin Frankel	source	10
SUGI	2004	To Rake or Not to Rake Is Not the Question Anymore with the Enhanced Raking Macro	David C. Hoaglin, Michael P. Battaglia	source	12
SUGI	2003	SAS Macros and Tools for Working with Weighted Logistic Regression Models That Use Survey Data	Annabella A. Battaglia, David C. Hoaglin, Michael P. Battaglia	source	10
NESUG	2002	Two Useful Macros to Nudge SAS to Serve You	Michael P. Battaglia		6
SUGI	2002	Use of the ROC Curve and the Bootstrap in Comparing Weighted Logistic Regression Models	Annabella Battaglia, David C. Hoaglin, Michael P. Battaglia	source	6
SUGI	2000	A SAS Macro for Balancing a Weighted Sample	Michael P. Battaglia, David C. Hoaglin	source	6
SUGI	1999	Transforming Multiple-record Data into Single-record Format When Number of Variables is Large	David Russo	source	6
NESUG	1998	Discussion Of Two Ways Of Transforming Multiple-Record Data Into Single-Record Format When There Is a Large Number Of Variables			6

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