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TABLE 7: NEGATIVE BINOMIAL FOR ENTRY

in Competition, Innovation, and Product Exit
by John M. de Figueiredo, John M. De Figueiredo, Margaret K. Kyle, Margaret K. Kyle

Table 1. Standard binomial model.

in Evidence and Credibility: Full Bayesian Significance Test for Precise Hypotheses
by Carlos Alberto, Bragança Pereira, Julio Michael Stern
"... In PAGE 5: ... The null hypothesis is defined as p H = q : . For all possible values of d, Table1 pres- ents the figures to compare our measure with the standard ones. To compute the Bayes Factor, we con-... ..."

Table 1. Rate of the size of log

in Accommodating Logical Logging under Fuzzy Checkpointing in Main Memory Databases
by Seungkyoon Woo, Myoung Ho Kim, Kim Yoon, Joon Lee 1997
Cited by 2

Table 2: Estimation using Negative Binomial Regression

in Abstract
by Michael Collins, Carrie Gates, Gaurav Kataria, Heinz School
"... In PAGE 7: ... We performed a negative binomial regression because the dependent variable (number of worms or number of variants released per month) fit well the negative binomial distribution. The results of the regression are reported in Table2 , column 2. Only the coefficients on dummy for FastTrack and eDonkey networks turned out to be significant predic- tors of worm count8.... In PAGE 7: ... Surprisingly, the coefficient on number of users was insignificant. A similar trend was observed for variant count data ( Table2 , column 3). An implicit assumption in the above estimation was that worm writers are focused only on P2P networks and respond directly to change in user population.... ..."

Table 2: Estimation using Negative Binomial Regression

in A Model for Opportunistic Network Exploits: The Case of P2P Worms
by Michael Collins, Carrie Gates, Gaurav Kataria
"... In PAGE 7: ... We performed a negative binomial regression because the dependent variable (number of worms or number of variants released per month) fit well the negative binomial distribution. The results of the regression are reported in Table2 , column 2. Only the coefficients on dummy for FastTrack and eDonkey networks turned out to be significant predic- tors of worm count8.... In PAGE 7: ... Surprisingly, the coefficient on number of users was insignificant. A similar trend was observed for variant count data ( Table2 , column 3). An implicit assumption in the above estimation was that worm writers are focused only on P2P networks and respond directly to change in user population.... ..."

Table 2: Checkability of the binomial regression model

in unknown title
by unknown authors 1998
"... In PAGE 8: ... The second stage assumes i i N ; 2 0 0 2 ; i = 1; :::; 20 with the illustrative informative priors, 2 IG(c; d) such that E( 2 ) = 10; V ar( 2 ) = 3; 2 IG(e; f) such that E( 2 ) = 1; V ar( 2 ) = 1 and N ??0 2 ; ?100 0 1 : Sampling based tting of this model is accomplished using Metropolis steps within a Gibbs sampler. Table2 summarizes the checkability of this model in terms of the I(d) and the interstage corre- lations using 1000 replications each providing 1000 pos- terior samples. We see that associations are weak, that d2j1 should be very e ective, d1 less so with the d2 apos;s o ering little promise.... ..."
Cited by 2

Table A: Binomial Logit Regression Coefficients

in An Empirical Examination of Entry Patterns in Local Telephone Markets
by James Zolnierek, James Eisner, Ellen Burton 2001
Cited by 1

Table 2. Binomial/highpass Filter Matrix

in Neural Chip SAND/1 for Real Time Pattern Recognition
by W. Eppler, T. Fischer, H. Gemmeke, T. Köder, R. Stotzka, Forschungszentrum Karlsruhe 1998
Cited by 1

Table 2: Rising k-binomial transforms

in The k-binomial transforms and the Hankel transform
by Michael Z. Spivey, Laura L. Steil
Cited by 1

Table 2: performs better follows the binomial distribution

in Do We Need Linguistics When We Have Statistics? A Comparative Analysis Of The Contributions Of Linguistic Cues To A Statistical Word Grouping System
by Vasileios Hatzivassiloglou Department
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