By J. A. Nelder (auth.), Robert Gilchrist (eds.)

ISBN-10: 0387907777

ISBN-13: 9780387907772

ISBN-10: 1461257719

ISBN-13: 9781461257714

This quantity of Lecture Notes in information includes the printed lawsuits of the 1st overseas convention to be hung on the subject of generalised linear versions. This convention used to be held from thirteen - 15 September 1982 on the Polytechnic of North London and marked a tremendous degree within the improvement and growth of the GLIM procedure. the diversity of the recent method, tentatively named Prism, is right here defined by way of Bob Baker. additional sections of the amount are dedicated to extra special descriptions of the recent amenities, together with details at the varied numerical tools now on hand. many of the information analyses during this quantity are conducted utilizing the GLIM method yet this is often, in fact, now not priceless. There are alternative ways of analysing generalised linear types and Peter eco-friendly right here discusses the various appealing gains of APL, together with its skill to examine generalised linear types. Later sections of the quantity disguise different invited and contributed papers at the idea and alertness of generalised linear versions. incorporated among those is a paper through Murray Aitkin, offering a unified method of statistical modelling via direct chance inference, and a paper by way of Daryl Pregibon displaying how GLIM might be programmed to hold out ranking exams. A paper through Joe Whittaker extends the new dialogue of the connection among conditional independence and log-linear versions and John Hinde considers the advent of an autonomous random variable right into a linear version to permit for unexplained edition in Poisson data.

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Additional resources for GLIM 82: Proceedings of the International Conference on Generalised Linear Models

Sample text

There is no algorithmic reason why the margin design matrix has to be orthogonal but if it is not then of course the marginal observations are not in general independent. X is permitted to give weighted sums. The marginal values are neither explicitly formed or stored and empty cells are ignored so that in general the overhead of $MARGIN is one of time rather than storage • • ELIMINATE [single term] has identical syntax but here the effect is to eliminate the specified term from all terms involved in subsequent fits.

Thus ,A is a 1-dimensiona1 array with the same data values as A. This can be necessary when combining arrays using the concatenation operator (,). A,B results in an array containing the data values of A and B combined. Concatenation is on the last dimension, thus matrices are combined column-wise. In general all arrays in concatenation must have the same number of dimensions, with all but the last conforming with respect to the number of levels. However a d-dimensiona1 and (d-1)dimensional array can be concatenated, the last dimension of the (d-1)-dimensiona1 array being taken to have one level.

1. ARRAY SHAPE. A variate. count or factor can be defined to be an array by attaching to it array attributes which define its shape. The shape specifies the number of dimensions and the number of levels for each dimension. In its simplest form a shape can be represented by a count variable with length corresponding to the number of dimensions and values the number of levels per dimension. 2) $VAR x///SHPE or the shape can be changed within array expressions. The array attributes are used only in directives APRINT.

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GLIM 82: Proceedings of the International Conference on Generalised Linear Models by J. A. Nelder (auth.), Robert Gilchrist (eds.)


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