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Biometrics, 1991
categorical data analysis , categorical data analysis , کتابخانه مرکزی دانشگاه علوم پزشکی ...
G. J. G. Upton, A. Agresti
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categorical data analysis , categorical data analysis , کتابخانه مرکزی دانشگاه علوم پزشکی ...
G. J. G. Upton, A. Agresti
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Jackknifing in Categorical Data Analysis
Australian Journal of Statistics, 1982SummaryEstimation of nonlinear functions of a multinomial parameter vector is necessary in many categorical data problems. The first and second order jackknife are explored for the purpose of reduction of bias. The second order jackknife of a function g(.) of a multinomial parameter is shown to be asymptotically normal if all second order partials ∂2g ...
Parr, William C., Tolley, H. Dennis
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The Analysis of Partially Categorized Contingency Data
Biometrics, 1974Summary: Sampling from multinomial populations in which classes are described according to two or more categories may, because of partial categorization of some of the observations, yield two or more related contingency tables. Estimation of parameters and tests of certain hypotheses for such partially categorized contingency data are considered and ...
Hocking, R. R., Oxspring, H. H.
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Annual Review of Psychology, 1998
▪ Abstract This chapter reviews recent developments in the analysis of categorical and contingency-table data. The first portion examines developments in model testing and selection. The second portion examines work on models for the structure of dependence.
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▪ Abstract This chapter reviews recent developments in the analysis of categorical and contingency-table data. The first portion examines developments in model testing and selection. The second portion examines work on models for the structure of dependence.
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2012
In the next two chapters we consider the kinds of categorical outcomes frequently encountered in epidemiological practice. Categorical variables are those that take on discrete values only. When there are only two possible values, such as survival vs. death, exposed vs. unexposed, or diseased vs. non-diseased, we can refer to them as dichotomous.
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In the next two chapters we consider the kinds of categorical outcomes frequently encountered in epidemiological practice. Categorical variables are those that take on discrete values only. When there are only two possible values, such as survival vs. death, exposed vs. unexposed, or diseased vs. non-diseased, we can refer to them as dichotomous.
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1995
In this chapter we will mainly be concerned with random variables whose outcomes are not ordinary numbers, but elements of several possible categories, classes or groups. The hair color or the eye color of a newborn baby are typical examples. A common problem is the question, whether there is a relationship between these categorical outcomes, for ...
Michael Falk +2 more
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In this chapter we will mainly be concerned with random variables whose outcomes are not ordinary numbers, but elements of several possible categories, classes or groups. The hair color or the eye color of a newborn baby are typical examples. A common problem is the question, whether there is a relationship between these categorical outcomes, for ...
Michael Falk +2 more
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[Characteristics of categorical data analysis].
Acta medica Croatica : casopis Hravatske akademije medicinskih znanosti, 2006This work presents characteristics of categorical data, their presentation and possible models of statistical analysis. There are two types of categorical data; nominal, where categories are equally valued (i.e. we can measure them only in terms of whether individual items belong to some distinctively different categories) and ordinal, where categories
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The Analysis of Categorical Data.
Operational Research Quarterly (1970-1977), 1975P. Macdonald, R. L. Plackett
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