Results 231 to 240 of about 2,337,681 (280)

DIFFICULTY FACTORS IN BINARY DATA

British Journal of Mathematical and Statistical Psychology, 1974
A number of writers have regarded difficulty factors as arising from the misbehaviour, in some sense, of correlation coefficients for binary data when the binary variables have varying difficulty levels. McDonald (1965) argued that difficulty factors are due to non‐linear item characteristic curves rather than difficulty
McDonald, Roderick P., Ahlawat, Kapur S.
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Modelling Binary Data

Technometrics, 1993
David J. Hand, D. Collett
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Estimating Intraclass Correlation for Binary Data

Biometrics, 1999
Summary.This paper reviews many different estimators of intraclass correlation that have been proposed for binary data and compares them in an extensive simulation study. Some of the estimators are very specific, while others result from general methods such as pseudo‐likelihood and extended quasi‐likelihood estimation.
Ridout, Martin S., Demétrio, Clarice G. B., Firth, David   +2 more
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The binary bootstrap: inference with autocorrelated binary data

Communications in Statistics - Simulation and Computation, 1993
We introduce the binary bootstrap for inference with autoconelated binary data. Weempirically evaluate the standard eirors and confidence intervals created with the binary bootstrap using four stochastic process with known results: Bernoulli trials, first-order Markov processes, and long customer delays in M/M/l and D/M/10 queues.
Yun Bae Kim, Jorge. Haddock, Thomas R. Willemain   +2 more
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Meta-analysis of Binary Data

2012
Meta-analyses can be defined as systematic reviews with pooled data. Because the separate studies in a meta-analysis have different sample sizes for the overall results a weighted average has to be calculated. Heterogeneity in a meta-analysis means that the differences in the results between the studies are larger than could happen by chance.
Ton J. Cleophas, Aeilko H. Zwinderman
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Binary Regression with Unreplicated Data

Biometrics, 1976
The results of a simulation study comparing the method of maximum likelihood for binary regression with unreplicated data and two approximate methods are presented and discussed. The two approximate methods are that of Cox [1966] and unweighted least squares with the 0's replaced by -3 and the l's by +3.
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Inference on binary images from binary data

Advances in Applied Probability, 1996
The problem addressed is to reverse the degradation which occurs when images are digitised: they are blurred, subjected to noise and rounding error, and sampled only at a lattice of points. Inference is considered for the fundamental case of binary scenes, binary data and isotropic blur.
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Exact Analysis for Paired Binary Data

Biometrics, 1994
This paper provides an efficient algorithm to generate exact distributions for the bivariate logistic model with common and sub-unit-specific covariates. The algorithm can be used to analyze correlated paired binary response data from studies lacking a large sample size.
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Binary Data Models

2012
In this chapter we consider the modeling of binary data. Such data are ubiquitous in many fields. Binary data present a number of distinct challenges, and so we devote a separate chapter to their modeling, though we lean heavily on the methods introduced in Chap. 6 on general regression modeling.
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An overview of real‐world data sources for oncology and considerations for research

Ca-A Cancer Journal for Clinicians, 2022
Lynne Penberthy, Donna R Rivera, Jennifer L Lund, Msph   +2 more
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