THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Generalized Linear Models (2nd ed.)

We present a catalog of high-energy gamma-ray sources detected by the Large Area Telescope (LAT), the primary science instrument on the Fermi Gamma-ray Space Telescope (Fermi), during the first 11 months of the science phase of the mission, which began on 2008 August 4. The First Fermi-LAT catalog (1FGL) contains 1451 sources detected and characterized in the 100 MeV to 100 GeV range. Source detection was based on the average flux over the 11 month period, and the threshold likelihood Test Statistic is 25, corresponding to a significance of just over 4 sigma. The 1FGL catalog includes source location regions, defined in terms of elliptical fits to the 95% confidence regions and power-law spectral fits as well as flux measurements in five energy bands for each source. In addition, monthly light curves are provided. Using a protocol defined before launch we have tested for several populations of gamma-ray sources among the sources in the catalog. For individual LAT-detected sources we provide firm identifications or plausible associations with sources in other astronomical catalogs. Identifications are based on correlated variability with counterparts at other wavelengths, or on spin or orbital periodicity. For the catalogs and association criteria that we have selected, 630 of the sources are unassociated. Care was taken to characterize the sensitivity of the results to the model of interstellar diffuse gamma-ray emission used to model the bright foreground, with the result that 161 sources at low Galactic latitudes and toward bright local interstellar clouds are flagged as having properties that are strongly dependent on the model or as potentially being due to incorrectly modeled structure in the Galactic diffuse emission.

/pdf/fermi-large-area-telescope-first-source-catalog-4kvrcbztnw.pdf

Fermi large area telescope first source catalog

A survey of recent developments in the theory and application of com- posite likelihood is provided, building on the review paper of Varin(2008). A range of application areas, including geostatistics, spatial extremes, and space-time mod- els, as well as clustered and longitudinal data and time series are considered. The important area of applications to statistical genetics is omitted, in light ofLarribe and Fearnhead(2011). Emphasis is given to the development of the theory, and the current state of knowledge on e!ciency and robustness of composite likelihood inference.

/pdf/an-overview-of-composite-likelihood-methods-2lgbmk7ee2.pdf

An overview of composite likelihood methods

The Bootstrap and Edgeworth Expansion

The Theory of Response-Adaptive Randomization in Clinical Trials: Hu/Response-Adaptive

Dedication. Preface. 1. Introduction. 1.1 Randomization in clinical trials. 1.2 Response-adaptive randomization in a historical context. 1.3 Outline of the book. 1.4 References. 2. Fundamental Questions of response-Adaptive Randomization. 2.1 Optimal allocation. 2.2 The realtionship between power and response-adaptive randomization. 2.3 The relationship for K > 2 treatments. 2.4 Asymptotically best procedures. 2.5 References. 3. Likelihood-based Inference. 3.1 Data structure and Likelihood. 3.2 Asymptotic properties of maximum likelihood estimators. 3.4 Conclusion. 3.5 References. 4. Procedures Based on Urn Models. 4.1 Generalized Friedman's urn. 4.2 The class of ternary urn models. 4.3 References. 5. Procedures Based on Sequential Estimation. 5.1 Examples. 5.2 Properties of procedures based on sequential estimation for K = 2. 5.3 Notation and conditions for the general framework. 5.4 Asymptotic results and some examples. 5.5 Proving the main theorems. 5.6 References. 6. Sample Size Calculation. 6.1 Power of a randomization procedure. 6.2 Three types of sample size. 6.3 Examples. 6.4 References. 7. Additional Considerations. 7.1 The effect of delayed response. 7.2 Continuous responses. 7.3 Multiple (K > 2) treatments. 7.4 Accommodating heterogeneity. 7.5 References. 8. Implications for the Practice of Clinical Trials. 8.1 Standards. 8.2 Binary response. 8.3 Continuous responses. 8.4 The effect of delayed response. 8.5 Conclusions. 8.6 References. 9. Incorporating Covariates. 9.1 Introduction and examples. 9.2 General framework and asymptotic results. 9.3 Generalized linear models. 9.4 Two treatments with binary responses. 9.5 Conclusions. 9.6 References. 10. Conclusions and Open Problems. 10.1 Conclusions. 10.2 Open problems. 10.3 References. Appendix A: Supporting Technical Material. A.1 Some matrix theory. A.2 Jordan decomposition. A.3 Matrix recursions. A.4 Martingales. A.5 Cramer-Wold device. A.6 Multivariate martingales. A.7 Multivariate Taylor's expansion. A.8 References. Appendix B: Proofs. B.1 Proofs theorems in Chapter 4. B.2 Proof of theorems in Chapter 5. B.3 Proof of theorems in Chapter 7. B.4 References. Author Index. Subject Index.

The Theory of Response-Adaptive Randomization in Clinical Trials

A general doubly adaptive biased coin design is proposed for the allocation of subjects to K treatments in a clinical trial. This design follows the same spirit as Efron's biased coin design and applies to the cases where the desired allocation proportions are unknown, but estimated sequentially. Strong consistency, a law of the iterated logarithm and asymptotic normality of this design are obtained under some widely satisfied conditions. For two treatments, a new family of designs is proposed and shown to be less variable than both the randomized play-the-winner rule and the adaptive randomized design. Also the proposed design tends toward a randomization scheme (with a fixed target proportion) as the size of the experiment increases.

/pdf/asymptotic-properties-of-doubly-adaptive-biased-coin-designs-2msoshu8ek.pdf

Asymptotic properties of doubly adaptive biased coin designs for multitreatment clinical trials

We provide a theoretical template for the comparison of response-adaptive randomization procedures for clinical trials. Using a Taylor expansion of the noncentrality parameter of the usual chi-squared test for binary responses, we show explicitly the relationship among the target allocation proportion, the bias of the randomization procedure from that target, and the variability induced by the randomization procedure. We also generalize this relationship for more than two treatments under various multivariate alternatives. This formulation allows us to directly evaluate and compare different response-adaptive randomization procedures and different target allocations in terms of power and expected treatment failure rate without relying on simulation. For K = 2 treatments, we compare four response-adaptive randomization procedures and three target allocations based on multiple objective optimality criteria. We conclude that the drop-the-loser rule and the doubly adaptive biased coin design are clearly super...

https://www.jstor.org/stable/pdfplus/30045294.pdf

Optimality, Variability, Power

Markov chain marginal bootstrap (MCMB) is a new method for constructing confidence intervals or regions for maximum likelihood estimators of certain parametric models and for a wide class of M estimators of linear regression. The MCMB method distinguishes itself from the usual bootstrap methods in two important aspects: it involves solving only one-dimensional equations for parameters of any dimension and produces a Markov chain rather than a (conditionally) independent sequence. It is designed to alleviate computational burdens often associated with bootstrap in high-dimensional problems. The validity of MCMB is established through asymptotic analyses and illustrated with empirical and simulation studies for linear regression and generalized linear models.

Feifang Hu

Papers

The Theory of Response-Adaptive Randomization in Clinical Trials: Hu/Response-Adaptive

The Theory of Response-Adaptive Randomization in Clinical Trials

Asymptotic properties of doubly adaptive biased coin designs for multitreatment clinical trials

Optimality, Variability, Power

Markov Chain Marginal Bootstrap