Regression Analysis: Classical and Bayesian

We are often interested in how some variable quantity depends on other variable quantities. We may ask, for instance, how the volume occupied by a gas is related to its temperature and pressure, or the boiling point of a fluid to the ambient pressure, or the level of outdoor crime to the amount of street illumination. Such relationships are often guessed at from specific values exhibited by the variables in experiments; but precisely how those guesses, or inferences, are made and justified is difficult and debatable. The difficulty is in part the familiar one that infinitely many different relationships are compatible with any actual

[1]  D. V. Lindley,et al.  The Bayesian Estimation of a Linear Functional Relationship , 1968 .

[2]  S. Chatterjee,et al.  Influential Observations, High Leverage Points, and Outliers in Linear Regression , 1986 .

[3]  A. L. Edwards,et al.  An introduction to linear regression and correlation. , 1985 .

[4]  J. Woodward.  Understanding Regression , 1988, PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association.

[5]  R. Gunst.  Regression analysis and its application , 1980 .

[6]  D. N. Sparks,et al.  Data Reduction: Analysing and Interpreting Statistical Data , 1976 .

[7]  James O. Berger,et al.  Statistical Analysis and the Illusion of Objectivity , 1988 .

[8]  J. Mackie.  MILLER'S SO-CALLED PARADOX OF INFORMATION , 1966, The British Journal for the Philosophy of Science.

[9]  Peter Urbach,et al.  Scientific Reasoning: The Bayesian Approach , 1989 .

[10]  D. Cox,et al.  Notes on Some Aspects of Regression Analysis , 1968 .

[11]  Sheldon H. Stein,et al.  Understanding Regression Analysis , 1990 .

[12]  S. Weisberg.  Plots, transformations, and regression , 1985 .

[13]  William L. Hays,et al.  An Introduction to Linear Regression and Correlation. 2nd ed. , 1985 .

[14]  Thomas H. Wonnacott,et al.  Regression: A Second Course in Statistics. , 1981 .

[15]  A. C. Atkinson.  [Influential Observations, High Leverage Points, and Outliers in Linear Regression]: Comment: Aspects of Diagnostic Regression Analysis , 1986 .

[16]  M. Greenwood.  An Introduction to Medical Statistics , 1932, Nature.

[17]  J. Wolfowitz,et al.  An Introduction to the Theory of Statistics , 1951, Nature.

[18]  E. Ziegel.  Regression: A Second Course in Statistics , 1988 .

[19]  J. W. Gorman,et al.  Fitting Equations to Data. , 1973 .

[20]  Christopher J. Nachtsheim.  Applied Regression Analysis and Experimental Design , 1987 .

[21]  H. Seal.  Studies in the history of probability and statistics. XV. The historical velopment of the Gauss linear model. , 1967, Biometrika.

[22]  W. W. Muir,et al.  Regression Diagnostics: Identifying Influential Data and Sources of Collinearity , 1980 .

[23]  J. Neyman.  Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability , 1937 .