Selection of Models of Lagged Identification Rates and Lagged Association Rates Using AIC and QAIC
نویسنده
چکیده
This article maybe used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. The lagged identification rate is the probability of identifying an individual given its identification some time lag earlier. The lagged association rate is the probability that two individuals are associated given their association some time lag earlier. Models of lagged identification and association rates fit by maximizing the sums of non independent log-likelihoods have approximately unbiased parameter estimates. Simulations suggest that: Akaike-Information-Criterion often selects the true model of lagged identification rate data; quasi-Akaike-Information-Criterion performs better for lagged association rates; and confidence intervals for parameters are best obtained by bootstrap methods for lagged identification rates and quasi-likelihood or jackknife methods for lagged association rates.
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عنوان ژورنال:
- Communications in Statistics - Simulation and Computation
دوره 36 شماره
صفحات -
تاریخ انتشار 2007