Analysis of monitoring data with many missing values: which method?
作者： C.J.F. ter Braak , A.J. van Strien , R. Meijer , T.J. Verstrael
DOI:
关键词: Log-linear model 、 Search engine indexing 、 Regression 、 Missing data 、 Poisson regression 、 Monitoring data 、 Computer science 、 Statistics 、 Imputation (statistics)
摘要: Large-scale monitoring of bird species becomes more and important in many countries. In the datasets yielded by these censuses, values are often missing. This poses problems analysis data. Currently five methods used to obtain yearly indices abundance trends over time: chain index, indexing according Mountford method, route regression, imputing missing data loglineair Poisson regression. Of each method advantages limitations dealt with. The loglinear regression appears be most promising approach. 1) DLO-Institute for Forestry Nature Research & DLO-Agricultural Mathematics Group, P.O. Box 100, 6700 AC Wageningen, Netherlands 2) Statistics Netherlands, 4000, 2270 JM Voorburg,
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