Record Nr.

UNINA9910144694903321

Autore

Chatterjee Samprit <1938->

Titolo

Sensitivity analysis in linear regression [[electronic resource] /] / Samprit Chatterjee, Ali S. Hadi

Pubbl/distr/stampa


New York, : Wiley, c1988

ISBN

1-282-30736-3

9786612307362

0-470-31676-4

0-470-31742-6

Descrizione fisica

1 online resource (341 p.)

Collana

Wiley series in probability and mathematical statistics. Applied probability and statistics

Altri autori (Persone)

HadiAli S

Disciplina

519.5

519.536

Soggetti

Regression analysis

Perturbation (Mathematics)

Mathematical optimization

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliography and index.

Nota di contenuto

Sensitivity Analysis in Linear Regression; PREFACE; Contents; 1. INTRODUCTION; 1.1. Introduction; 1.2. Notations; 1.3. Standard Estimation Results in Least Squares; 1.4. Assumptions; 1.5. Iterative Regression Process; 1.6. Organization of the Book; 2. PREDICTION MATRIX; 2.1.Introduction; 2.2. Roles of P and (I -P) in Linear Regression; 2.3. Properties of the Prediction Matrix; 2.3.1. General Properties; 2.3.2. Omitting (Adding) Variables; 2.3.3. Omitting (Adding) an Observation; 2.3.4. Conditions for Large Values of pii; 2.3.5. Omitting Multiple Rows of X; 2.3.6. Eigenvalues of P and (I- P)

2.3.7. Distribution of pü2.4. Examples; 3. ROLE OF VARIABLES IN A REGRESSION EQUATION; 3.1. Introduction; 3.2. Effects of Underfitting; 3.3. Effects of Overfining; 3.4. Interpreting Successive Fining; 3.5. Computing Implications for Successive Fitting; 3.6. Introduction of One Additional Regressor; 3.7. Comparing Models: Comparison Criteria; 3.8. Diagnostic Plots for the Effects of Variables; 3.8.1. Added Variable (Partial Regression) Plots; 3.8.2. Residual Versus Predictor Plots; 3.8.3.

Component-Plus-Residual (Partial Residual) Plots; 3.8.4. Augmented Partial Residual Plots

3.9. Effects of an Additional Regressor4. EFFECTS OF AN OBSERVATION ON A REGRESSION EQUATION; 4.1. Introduction; 4.2. Omission Approach; 4.2.1. Measures Based on Residuals; 4.2.1.1. Testing for a Single Outlier; 4.2.1.2. Graphical Methods; 4.2.2. Outliers, High-leverage, and Influential Points; 4.2.3. Measures Based on Remoteness of Points in X-Y Space; 4.2.3.1. Diagonal Elements of P; 4.2.3.2. Mahalanobis Distance; 4.2.3.3. Weighted Squared Standardized Distance; 4.2.3.4. Diagonal Elements of Pz; 4.2.4. Influence Curve; 4.2.4.1. Definition of the Influence Curve

4.2.4.2. Influence Curves for β and σ24.2.4.3. Approximating the Influence Curve; 4.2.5. Measures Based on the Influence Curve; 4.2.5.1. Cook's Distance; 4.2.5.2. Welsch-Kuh's Distance; 4.2.5.3. Welsch's Distance; 4.2.5.4. Modified Cooks Distance; 4.2.6. Measures Based on the Volume of Confidence Ellipsoids; 4.2.6.1. Andrews-Pregibon Statistic; 4.2.6.2. Variance Ratio; 4.2.6.3. Cook-Weisberg Statistic; 4.2.7. Measures Based on the Likelihood Function; 4.2.8. Measures Based on a Subset of the Regression Coefficients; 4.2.8.1. Influence on a Single Regression Coefficient

4.2.8.2. Ilnfluence on Linear Functions of β4.2.9. Measures based on the Eigensmcture of X; 4.2.9.1. Condition Number and Collinearity Indices; 4.2.9.2. Collinearity-Influential Points; 4.2.9.3. Effects of an Observation on the Condition Number; 4.2.9.4. Diagnosing Collinearhy-Influential Observations; 4.3. Differentiation Approach; 4.4. Summary and Concluding Remarks; 5. ASSESSING THE EFFECTS OF MULTIPLE OBSERVATIONS; 5.1. Introduction; 5.2. Measures Based on Residuals; 5.3. Measures Based on the Influence Curve; 5.3.1. Sample lnfluence Curve; 5.3.2. Empirical Influence Curve

5.3.3. Generalized Cook's Distance

Sommario/riassunto

Treats linear regression diagnostics as a tool for application of linear regression models to real-life data. Presentation makes extensive use of examples to illustrate theory. Assesses the effect of measurement errors on the estimated coefficients, which is not accounted for in a standard least squares estimate but is important where regression coefficients are used to apportion effects due to different variables. Also assesses qualitatively and numerically the robustness of the regression fit.

Record Nr.

UNINA9910778744303321

Titolo

Transnational organized crime [[electronic resource] ] : summary of a workshop / / Committee on Law and Justice, Commission on Behavioral and Social Sciences and Education, National Research Council ; Peter Reuter and Carol Petrie, editors

Pubbl/distr/stampa


Washington, D.C., : National Academy Press, c1999

ISBN

0-309-18445-2

0-309-51991-8

0-585-08577-3

Descrizione fisica

1 online resource (76 p.)

Collana

Compass series

Altri autori (Persone)

ReuterPeter <1944->

PetrieCarol

Disciplina

364.1/06

Soggetti

Transnational crime

Organized crime