By James K. Lindsey
The target of this ebook is to offer a survey of the various ways that the statistical package deal GLIM can be used to version and research stochastic procedures. Its emphasis is on utilizing GLIM interactively to use statistical innovations, and examples are drawn from quite a lot of functions together with medication, biology, and the social sciences. it truly is in response to the author's a long time of training classes alongside those traces to either undergraduate and graduate scholars. the writer assumes that readers have a fairly robust history in facts resembling will be won from undergraduate classes and they also are acquainted with the elemental workings of GLIM. issues lined contain: the research of survival facts, regression and becoming distributions, time sequence research (including either the time and frequency domains), repeated measurements, and generalized linear models.
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Extra resources for The Analysis of Stochastic Processes using GLIM
They are non-parametric. We must first classify the data in ascending order of interval lengths, and then apply the macro called KMSP to obtain the survival curve. 1. 000 11. 000 I FINT 31. 000 1. 000 71. 5 $Use KMSP i cens tint 9 Kaplan-Meier Survivor Function Estimates Time Group No. 000 1. 000 11. 000 41. 000 71. 000 1. 000 Plot of set) vs. 00000000 - Empirical Survivor CUrve(s) 1. 0 Here we only present the ftrst part of the output from the macro, the survivor curve and the hazard function which interest us, reserving the rest for the next section.
F. e. 000 We see that the best individual fit is given by the square of the log of the time intervals. We add the log intervals, since the combination of these two corresponds to a log nonnal distribution, and then plot the resulting distribution. f. f. ·e.
We set up the data. The first model fitted is the usual one for independence: $UNits 16 $DAta freq $DINput 41 $INput 42 SMCT $Yvariate freq $ERror P $CAlculate m71=OfoGL(4,1) : m66=OfoGL(4,4) : OfoR=1 $FActor m66 4 m71 4 $Use MPCT m66 m71 1. f. = Chi2 probability = 19884. at cycle 9 o. 6 for Chi2 = 19884. e. 08141 scale parameter taken as 1. 000 9. f. 126 As would be expected, this model is definitely not acceptable. We note the very large underestimation of all diagonal cells. The macro gives a zero weight to the diagonal elements to refit the (quasi-) independence model: 3.