An N⋅log(N) method for evaluating electrostatic energies and forces of large periodic systems is presented. The method is based on interpolation of the reciprocal space Ewald sums and evaluation of the resulting convolutions using fast Fourier transforms. Timings and accuracies are presented for three large crystalline ionic systems.

Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems

The technique of iterative weighted linear regression can be used to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation. A generalization of the analysis of variance is given for these models using log- likelihoods. These generalized linear models are illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables) and gamma (variance components).

Generalized Linear Models

Summary: The program MODELTEST uses log likelihood scores to establish the model of DNA evolution that best fits the data. Availability: The MODELTEST package, including the source code and some documentation is available at http://bioag.byu.edu/zoology/crandall―lab/modeltest.html. Contact: dp47@email.byu.edu.

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MODELTEST: testing the model of DNA substitution.

This paper presents a fully specified model of long-run growth in which knowledge is assumed to be an input in production that has increasing marginal productivity. It is essentially a competitive equilibrium model with endogenous technological change. In contrast to models based on diminishing returns, growth rates can be increasing over time, the effects of small disturbances can be amplified by the actions of private agents, and large countries may always grow faster than small countries. Long-run evidence is offered in support of the empirical relevance of these possibilities.

/pdf/increasing-returns-and-long-run-growth-1wijc940aq.pdf

Increasing Returns and Long-Run Growth

The main intent of this paper is to introduce a new statistical procedure for testing a complete sample for normality. The test statistic is obtained by dividing the square of an appropriate linear combination of the sample order statistics by the usual symmetric estimate of variance. This ratio is both scale and origin invariant and hence the statistic is appropriate for a test of the composite hypothesis of normality. Testing for distributional assumptions in general and for normality in particular has been a major area of continuing statistical research-both theoretically and practically. A possible cause of such sustained interest is that many statistical procedures have been derived based on particular distributional assumptions-especially that of normality. Although in many cases the techniques are more robust than the assumptions underlying them, still a knowledge that the underlying assumption is incorrect may temper the use and application of the methods. Moreover, the study of a body of data with the stimulus of a distributional test may encourage consideration of, for example, normalizing transformations and the use of alternate methods such as distribution-free techniques, as well as detection of gross peculiarities such as outliers or errors. The test procedure developed in this paper is defined and some of its analytical properties described in ? 2. Operational information and tables useful in employing the test are detailed in ? 3 (which may be read independently of the rest of the paper). Some examples are given in ? 4. Section 5 consists of an extract from an empirical sampling study of the comparison of the effectiveness of various alternative tests. Discussion and concluding remarks are given in ?6. 2. THE W TEST FOR NORMALITY (COMPLETE SAMPLES) 2 1. Motivation and early work This study was initiated, in part, in an attempt to summarize formally certain indications of probability plots. In particular, could one condense departures from statistical linearity of probability plots into one or a few 'degrees of freedom' in the manner of the application of analysis of variance in regression analysis? In a probability plot, one can consider the regression of the ordered observations on the expected values of the order statistics from a standardized version of the hypothesized distribution-the plot tending to be linear if the hypothesis is true. Hence a possible method of testing the distributional assumptionis by means of an analysis of variance type procedure. Using generalized least squares (the ordered variates are correlated) linear and higher-order

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An Analysis of Variance Test for Normality (Complete Samples)

Multivariate Analysis (2nd ed.).

Studies in the History of Statistics and Probability

Studies in the history of statistics and probability: A series of papers

Preface to the Sixth Edition. List of Examples. Glossary of Abbreviations. 17. Estimation and Sufficiency. 18. Estimation: Maximum Likelihood and Other Methods. 19. Interval Estimation. 20. Tests of Hypotheses: Simple Null Hypotheses. 21. Tests of Hypotheses: Composite Hypotheses. 22. Likelihood Ratio Tests and Test Efficiency. 23. Invariance and Equivariance. 24. Sequential Methods. 25. Tests of Fit. 26. Comparative Statistical Inference. 27. Statistical Relationship: Linear Regression and Correlation. 28. Partial and Multiple Correlation. 29. The General Linear Model. 30. Fixed Effects Analysis of Variance. 31. Other Analysis of Variance Models. 32. Analysis and Diagnostics for the Linear Model. Appendix Tables. References. Index of Examples in Text. Author Index. Subject Index.

Maurice G. Kendall

Papers

Multivariate Analysis (2nd ed.).

Studies in the History of Statistics and Probability

Studies in the history of statistics and probability: A series of papers

Classical inference and the linear model

Design and analysis, and time-series