Showing papers on "Unit-weighted regression published in 1990"

Applied Regression Analysis for Business and Economics

Abstract: 1. AN INTRODUCTION TO REGRESSION ANALYSIS 2. REVIEW OF BASIC STATISTICAL CONCEPTS Introduction / Descriptive Statistics / Discrete Random Variables and Probability Distributions / The Normal Distribution / Populations, Samples, and Sampling Distributions / Estimating a Population Mean / Hypothesis Tests about a Population Mean / Estimating the Difference Between Two Population Means / Hypothesis Tests about the Difference Between Two Population Means / Using the Computer 3. SIMPLE REGRESSION ANALYSIS Using Regression Analysis to Describe a Linear Relationship / Examples of Regression as a Descriptive Technique / Inferences from a Simple Regression Analysis / Assessing the Fit of the Regression Line / Prediction or Forecasting with a Simple Linear Regression Equation / Fitting a Linear Trend to Time-Series Data / Some Cautions in Interpreting Regression Results / Using the Computer 4. MULTIPLE REGRESSION ANALYSIS Using Multiple Regression to Describe a Linear Relationship / Inferences from a Multiple Regression Analysis / Assessing the Fit of the Regression Line / Comparing Two Regression Models / Prediction with a Multiple Regression Equation / Lagged Variables as Explanatory Variables in Time-Series Regression / Using the Computer 5. FITTING CURVES TO DATA Introduction / Fitting a Curvilinear Relationship / Using the Computer 6. ASSESSING THE ASSUMPTIONS OF THE REGRESSION MODEL Introduction / Assumptions of the Multiple Linear Regression Model / The Regression Residuals / Assessing the Assumption That the Relationship is Linear / Assessing the Assumption That the Variance Around the Regression Line is Constant / Assessing the Assumption That the Disturbances Are Normally Distributed / Influential Observations / Assessing the Assumption That the Disturbances Are Independent / Multicollinearity / Using the Computer 7. USING INDICATOR AND INTERACTION VARIABLES Using and Interpreting Indicator Variables / Interaction Variables / Seasonal Effects in Time-Series Regression / Using the Computer 8. VARIABLE SELECTION Introduction to Variable Selection / All-Possible Regressions / Other Variable Selection Techniques / Which Variable Selection Procedure is Best? / Using the Computer 9. INTRODUCTION TO ANALYSIS OF VARIANCE One-Way Analysis of Variance / Analysis of Variance Using a Randomized Block Design / Two-Way Analysis of Variance / Analysis of Covariance / Using the Computer. (Part contents).

Diagnostics in transformation and weighted regression

Abstract: We describe a diagnostic method for assessing the influence of an individual case on the transformation power estimator in the Box–Cox regression model and transform-both-sides regression model (Carroll and Ruppert 1988). We compare the method to those proposed by Cook and Wang (1983) and Hinkley and Wang (1988). The new method takes into account the deletion effect on the Jacobian of the variable's transformation. It provides a more accurate and reliable transformation power estimator. We also extend this method to analyze the case influence on the weighted parameter estimators in both the weighted regression model and the transformed and weighted regression model. Several examples are used to illustrate the results.

Empirical bayes test of regression coefficient in a multiple linear regression model

Abstract: Recently R. S. Singh has S~udied the empirical Bayes (EB) es~ima,ion in a mul~iple linear regression model. In ~his paper we consider the EB ~est of regression coefficient ~ for this model. We work out the EB test decision rule by using kernel estimation of multivariate density function and i~ firs~ order partial derivatives. We obtain i~ asymptotically optimal (a. o.) property under the condition ~l]~]l,<¢~. I t is shown that the convergence ra~es of this EB ~ t decision rule are _{r-1}A pr 0 (n ~--'~7"_~ ) under the condition E ~ = X ' < ¢ ~ , where an integer r > l , 0 < ~ < 1 and p is the ~mension of the vector.