Quadrat Analysis_RW_Thomas

For instance in order to obtain the maximum

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Unformatted text preview: it is necessary to carry out a complex iterative procedure which is too involved to incorporate in this monograph. The reader who wishes to follow up this topic is referred to a paper by Bliss and Fisher (1953) which gives a clear account of maximum likelihood estimation for the negative binomial k parameter. Table 5 (ii) shows the results of fitting the negative binomial to the innovation adoption data by maximum likelihood methods, and the results illustrate well the different principles involved in the two estimation procedures. obtained for the two largest frequency classes and this in fit has been occurs because the likelihood function is especially sensitive to large values 22 23 'modal' estimation. It may also be noted that the maximum likelihood estimate does not preserve the value of the observed variance in the model prediction. from both populations. is aiven by (52) clude that the frequency distribution of innovation adopters is the result of a contagious process. However, in many applications of the negative binomial the two estimation procedures yield widely differin...
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This note was uploaded on 02/15/2012 for the course GEO 6938 taught by Professor Staff during the Summer '08 term at University of Florida.

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