Statistical research spans an enormous range from direct subject-matter collaborations to pure mathematical theory. The Annals of Applied Statistics, the newest journal from the IMS, is aimed at papers in the applied half of this range. Published quarterly in both print and electronic form, our goal is to provide a timely and unified forum for all areas of applied statistics.
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11 to 20 of 46 Results
Adobe PDF - 40.4 KB - MD5: 03766e6ae824504d71b17fb24df8fd6c
1. Documentation
WinBUGS code for the univariate spatial stick-breaking model
Nov 27, 2007
Michael A. Newton; Fernando A. Quintana; ohan A. den Boon; Srikumar Sengupta; and Paul Ahlquist, 2007, "Replication data for: Random-set Methods Identify Distinct Aspects of the Enrichment Signal in Gene-set Analysis", https://doi.org/10.7910/DVN/ZVHDGA, Harvard Dataverse, V1
A prespecified set of genes may be enriched, to varying degrees, for genes that have altered expression levels relative to two or more states of a cell. Knowing the enrichment of gene sets defined by functional categories, such as gene ontology (GO) annotations, is valuable for analyzing the biological signals in microarray expression data. A commo...
Adobe PDF - 388.5 KB - MD5: 2b1f9cc90a02328ef24ac155fa26ec9a
1. Documentation
Supplementary Materials for Newton et al.
Nov 27, 2007
Galit Shmueli; Ralph P. Russo; and Wolfgang Jank, 2007, "Replication data for: The BARISTA: A Model for Bid Arrivals in Online Auctions", https://doi.org/10.7910/DVN/FQ3CCH, Harvard Dataverse, V1
The arrival process of bidders and bids in online auctions is important for studying and modeling supply and demand in the online marketplace. A popular assumption in the online auction literature is that a Poisson bidder arrival process is a reasonable approximation. This approximation underlies theoretical derivations, statistical models, and sim...
Adobe PDF - 386.1 KB - MD5: 731a8da19506acdfbf3dfd1e245b4525
1. Documentation
Original article for this study
Plain Text - 340 B - MD5: 7cfff3cc5705d585a92dade43ce22af7
2. Data
Computes the CDF(threshold) = P(X <= threshold) for a given dataset
Plain Text - 1016 B - MD5: 99cb3afbf0bf77f8ab2587ffdec86ad8
2. Data
Computes CDF-based (quick&crude) estimate of alpha3 in [T-d2,T] based on s,t within this interval, and gives a standard error based on bootstrap
Plain Text - 1.0 KB - MD5: fb4de4570961118712ae9d75df261d84
2. Data
Estimates alpha1 and alpha2
Plain Text - 1.2 KB - MD5: 478c6bb7f067fc2e5e8ab99519886c6e
2. Data
Computes quick&dirty estimate of alpha in a chosen interval ([0,d1], [d1-T-d2], or [T-d2,T]) based on two values T-s and T-t within this interval. The program gives an estimate and a standard error based on bootstrap
Plain Text - 1.9 KB - MD5: 59fd2667bd605d16359448f90fcdeda2
2. Data
Computes CDF-based (quick&crude) estimates of the change-points d1, d2 and their standard error based on bootstrap
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