Qualitative aspects of large scale systems : developing design rules using APL

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Qualitative aspects of large scale systems : developing design rules using APL / O.I. Franksen, P. Falster, F.J. Evans

資料種別:

図書

出版情報:

Berlin ; New York : Springer-Verlag, 1979

形態:

xii, 119 p. ; 24 cm

シリーズ名:

Lecture notes in control and information sciences ; 17 <BA00216932>

著者名:

ISBN:

9780387096094 [0387096094] (: U.S. : pbk.)
9783540096092 [3540096094] (: gw)

書誌ID:

BA06403706

子書誌情報

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書誌詳細

注記:

Bibliography: p. 118-119

主題:

APL (computer program language); Large scale systems; System design

言語:

英語

目次情報:

Introduction / 1：

Marginal Models for Categorical Data / 1.1：

Historical and Comparable Approaches / 1.2：

Coefficients for the Comparsion of Marginal Distributions / 1.3：

Measuring Differences in Location / 1.3.1：

Measuring Differences in Dispersion / 1.3.2：

Measuring Association / 1.3.3：

Measuring Agreement / 1.3.4：

Loglinear Marginal Models / 2：

Ordinary Loglinear Models / 2.1：

Basic Concepts and Notation / 2.1.1：

Modeling Association Among Three Variables / 2.1.2：

Applications of Loglinear Marginal Models / 2.2：

Research Questions and Designs Requiring Marginal Models / 2.2.1：

Comparing One Variable Distributions / 2.2.2：

More Complex Designs and Research Questions / 2.2.3：

Maximum Likelihood Inference for Loglinear Marginal Models / 2.3：

Sampling Methods / 2.3.1：

Specifying Loglinear Marginal Models by Constraining the Cell Probalities / 2.3.2：

Simultaneous Modeling of Joint and Marginal Distributions: Redundancy, Incompatibility and Other Issues / 2.3.3：

***Maximum Likelihood Estimates of Constrained Cell Probabilities / 2.3.4：

***A Numerical Algorithm for ML Estimation / 2.3.5：

***Efficient Computation of ML Estimates for Simultaneous Joint and Marginal Models / 2.3.6：

***Large Sample Distribution of ML estimates / 2.3.7：

Model Evaluation / 2.3.8：

Nonloglinear Marginal Models / 3：

Comparing Item Characteristics for Different Measurement Levels / 3.1：

Interval Level of Measurement / 3.1.1：

Ordinal Level of Measurement / 3.1.2：

Nominal Level of Measurement / 3.1.3：

Comparing Associations / 3.2：

Maximum Likelihood Estimation / 3.3：

Generalized exp-log Specification of Nonloglinear Marginal Models / 3.3.1：

Compatibility and Redundancy of Restrictions / 3.3.2：

Homogeneous Specification of Coefficients / 3.3.3：

***Algorithm for Maximum Likelihood Estimation / 3.3.4：

***Asymptotic Distribution of ML Estimates / 3.3.5：

Marginal Analysis of Longitudinal Data / 4：

Trend Data / 4.1：

Comparing Net Changes in More Than One Characteristic / 4.1.1：

Simultaneous Tests for Restrictions on Association and Net Change: Modeling Joint and Marginal Tables / 4.1.2：

Panel Data: Investigating Net Changes in One Characteristic / 4.2：

Overall Net Changes; Cumulative Proportions; Growth Curves / 4.2.1：

Subgroup Comparisons of Net Changes / 4.2.2：

Changes in Associations / 4.2.3：

Gross Changes in One Characteristic / 4.3：

Comparing Turnover Tables for Different Periods / 4.3.1：

Comparing Summary Measures of Gross Change / 4.3.2：

Extensions; Net Plus Gross Changes; Multiway Turnover Tables; Subgroup Comparisons / 4.3.3：

Net and Gross Changes in Two Related Characteristics / 4.4：

Net Changes in Two Characteristics / 4.4.1：

Changes in Association Between Two Changing Characteristics / 4.4.2：

Gross Changes in Two Characteristics / 4.4.3：

Combining Hypotheses about Net and Gross Changes / 4.4.4：

Minimally Specified Models for Comparing Tables with Overlapping Marginals; Detection of Problematic Models / 4.5：

Causal Analyses: Structural Equation Models and (Quasi-) Experimental Designs / 5：

SEMs - Structural Equation Models / 5.1：

SEMs for Categorical Data / 5.1.1：

An Example: Women's Role / 5.1.2：

Marginal Modeling and Categorical SEM / 5.1.3：

Analysis of (Quasi-)Experimental Data / 5.2：

The One-group Pretest-Posttest Design / 5.2.1：

The Nonequivalent Control Group Design / 5.2.2：

A Truly Experimental Design / 5.2.3：

Marginal Modeling with Latent Variables / 6：

Latent Class Models / 6.1：

Latent Marginal Homogeneity / 6.2：

Loglinear and Nonloglinear Latent Class Models: Equal Reliabilities / 6.3：

Restrictions on Conditional Responses Probabilities / 6.3.1：

Restrictions on Odds Ratios / 6.3.2：

Restrictions on Percentage Differences / 6.3.3：

Restrictions on Agreement / 6.3.4：

Marginal causal analyses / 6.4：

SEMs with latent marginal homogeneity / 6.4.1：

Latent Variable SEMs for Clustered Data / 6.4.2：

Estimation of Marginal Models with Latent Variables Using the EM Algorithm / 6.5：

Basic EM Algorithm / 6.5.1：

***General EM for Marginal Models / 6.5.2：

***Marginal Restrictions in Combination with a Loglinear Model for the Complete Table / 6.5.3：

***Speeding up of the EM Algorithm for Separable Models / 6.5.4：

Conclusions, Extensions, and Applications / 6.5.5：

Marginal Models for Continuous Variables / 7.1：

Changes in Means / 7.1.1：

Changes in Correlation and Regression Coefficients / 7.1.2：

Alternative Procedures and Models / 7.2：

Alternative Estimation Procedures: WLS and GEE / 7.2.1：

Modeling Dependent Observations: Marginal, Random Effects, and Fixed Effects Models / 7.2.2：

Specific Applications / 7.3：

Multiple Responses / 7.3.1：

Categorical Dyadic Data / 7.3.2：

Mokken Scale Analysis / 7.3.3：

Problems and Future Developments / 7.4：

Software, Generalized exp-log Routines, and Website / 7.5：

References

Author Index

Subject Index

Introduction / 1：

Marginal Models for Categorical Data / 1.1：

Historical and Comparable Approaches / 1.2：

Coefficients for the Comparsion of Marginal Distributions / 1.3：

Measuring Differences in Location / 1.3.1：

Measuring Differences in Dispersion / 1.3.2：

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