Alan Agresti(auth.)'s Analysis of Ordinal Categorical Data, Second Edition PDF

By Alan Agresti(auth.)

ISBN-10: 0470082895

ISBN-13: 9780470082898

ISBN-10: 0470594004

ISBN-13: 9780470594001

Statistical science’s first coordinated guide of equipment for reading ordered express facts, now absolutely revised and up-to-date, keeps to give purposes and case stories in fields as different as sociology, public health and wellbeing, ecology, advertising, and pharmacy. Analysis of Ordinal specific information, moment Edition presents an advent to simple descriptive and inferential equipment for specific information, giving thorough assurance of latest advancements and up to date tools. particular emphasis is put on interpretation and alertness of tools together with an built-in comparability of the to be had concepts for studying ordinal information. Practitioners of information in executive, (particularly pharmaceutical), and academia will wish this new edition.Content:
Chapter 1 creation (pages 1–8):
Chapter 2 Ordinal chances, rankings, and Odds Ratios (pages 9–43):
Chapter three Logistic Regression types utilizing Cumulative Logits (pages 44–87):
Chapter four different Ordinal Logistic Regression versions (pages 88–117):
Chapter five different Ordinal Multinomial reaction versions (pages 118–144):
Chapter 6 Modeling Ordinal organization constitution (pages 145–183):
Chapter 7 Non?Model?Based research of Ordinal organization (pages 184–224):
Chapter eight Matched?Pairs facts with Ordered different types (pages 225–261):
Chapter nine Clustered Ordinal Responses: Marginal versions (pages 262–280):
Chapter 10 Clustered Ordinal Responses: Random results versions (pages 281–314):
Chapter eleven Bayesian Inference for Ordinal reaction facts (pages 315–344):

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Extra resources for Analysis of Ordinal Categorical Data, Second Edition

Sample text

For odds ratio measures, it is sensible to do this on the log scale. Let Z denote the control variable(s) (or variable identifying the various studies in a meta-analysis). Let K denote the number of categories of Z, which is also the number of partial tables. Possible choices for the weights {wk\ include: • Wk = l/K, equal weight to the sample measure in each table. • Wk — n++k/n, the proportion of observations in the partial table. • Wk = (l/SE^AX^L, 1/SE^), where SE* is the estimated standard error of the sample measure in partial table k.

A confidence interval for a implies a corresponding confidence interval for Δ = P{Y\ > Y2) — P(Y2>Yi), since Δ = 2a - 1. For independent multinomial samples of sizes n\ and ni from the two rows, Halperin et al. 14) where X i= l V j=i+1 j 1 - j=2 x Z - Pc\\Pc\2 J ^2 PIIPW i= l The Wald approach works better by applying it to estimate logit a rather than a. 96—. a ( l — a) Its bounds [LB, UB] induce the interval exp(LB) l+exp(LB)' exp(UB) l+exp(UB) for a. If ά is either 0 or 1, the interval is undefined, and it is better to use the interval obtained with the profile likelihood or score method.

However, that method and the mid-P-based conditional method are not yet available in standard software. 8, from one protocol for a study at the Dana Färber Cancer Institute. For patients receiving a bone marrow transplant, the ordinal response was the severity of graft versus host disease (GVHD). 8 covers a suspected risk factor: whether there was a type of blood incompatibility, called a MHC mismatch, between the donor and the recipient of the bone marrow. 25, = oo. 8. Severity of GVHD in Leukemia Patients by Whether Patient Had MHC Mismatch Severity of GVHD Toxicity None Mild Moderate Severe Extreme Total Mismatch Match 2 3 2 4 2 1 1 2 2 0 8 10 Total 5 6 3 3 2 18 MHC Status Source: StatXact (2005, p.

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Analysis of Ordinal Categorical Data, Second Edition by Alan Agresti(auth.)


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