Statistical Analysis With Missing Data
Preface.PART I: OVERVIEW AND BASIC APPROACHES.Introduction.Missing Data in Experiments.Complete-Case and Available-Case Analysis, Including Weighting Methods.Single Imputation M...
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Preface.PART I: OVERVIEW AND BASIC APPROACHES.Introduction.Missing Data in Experiments.Complete-Case and Available-Case Analysis, Including Weighting Methods.Single Imputation M...
The attainment of quality in products and services has become a pivotal concern of the 1980s. While quality in tangible goods has been described and measured by marketers, quali...
Abstract NAMD is a parallel molecular dynamics code designed for high‐performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high‐end par...
Abstract Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population‐based c...
A contracted Gaussian basis set (6-311G**) is developed by optimizing exponents and coefficients at the Mo/ller–Plesset (MP) second-order level for the ground states of first-ro...
Abstract How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been th...
Abstract Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on ca...
This paper describes a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction. It also...
Part 1 Probability and Random Variables 1 The Meaning of Probability 2 The Axioms of Probability 3 Repeated Trials 4 The Concept of a Random Variable 5 Functions of One Random V...
Previous attempts to combine Hartree–Fock theory with local density-functional theory have been unsuccessful in applications to molecular bonding. We derive a new coupling of th...
We show how to use “complementary priors” to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. U...