Nancy Reid

Bio: Nancy Reid is an academic researcher from University of Toronto. The author has contributed to research in topics: Inference & Frequentist inference. The author has an hindex of 35, co-authored 119 publications receiving 6990 citations. Previous affiliations of Nancy Reid include University of British Columbia & University of York.

Parameter Orthogonality and Approximate Conditional Inference

Abstract: On propose une statistique du rapport de vraisemblance construite a partir de la distribution conditionnelle des observations, etant donne les estimateurs du maximum de vraisemblance pour les parametres de nuisance

An overview of composite likelihood methods

Abstract: 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.

Composite Likelihood Methods

Abstract: Composite likelihood methods are extensions of the Fisherian likelihood theory, one of the most influential approaches in statistics. Such extensions are generally motivated by the issue of computational feasibility arising in the application of the likelihood method in high-dimensional data analysis. Complex dependence presents substantial challenges in statistical modelling and methods and in substantive applications. The idea of projecting high-dimensional complicated likelihood functions to low-dimensional computationally feasible likelihood objects is methodologically appealing. Composite likelihood inherits many of the good properties of inference based on the full likelihood function, but is more easily implemented with high-dimensional data sets. This methodology is, to some extent, an alternative to the Markov Chain Monte Carlo method, and its impact is unbounded. The literature on both theoretical and practical issues for inference based on composite likelihood continues to expand quickly; the field of extremal processes for spatial data, of particular importance for climate modelling, is one of the most recent examples of an area where composite likelihood inference is both practical and efficient. The first international workshop on composite likelihood methods was held at the University of Warwick in April 2008. It attracted participants from all over the world and was widely viewed as very successful. Following the workshop, a special issue of the journal Statistica Sinica devoted to composite likelihood was announced; it was published in January 2011. This issue includes two long overview papers, one of which is devoted to applications in statistical genetics; several papers developing new theory for inference based on composite likelihood; new results in the application of composite likelihood to time series, spatial processes, longitudinal data and missing data. The methodology has drawn considerable attention in a broad range of applied disciplines in which complex data structures arise. Some notable application areas include, statistical genetics, genetic epidemiology, finance, panel surveys, computer experiments, geostatistics and biostatistics.

A note on pseudolikelihood constructed from marginal densities

Abstract: For likelihood-based inference involving distributions in which high-dimensional dependencies are present it may be useful to use approximate likelihoods based, for example, on the univariate or bivariate marginal distributions. The asymptotic properties of formal maximum likelihood estimators in such cases are outlined. In particular, applications in which only a single q x I vector of observations is observed are examined. Conditions under which consistent estimators of parameters result from the approximate likelihood using only pairwise joint distributions are studied. Some examples are analysed in detail.

The Theory of the Design of Experiments

Abstract: SOME GENERAL CONCEPTS Types of Investigation Observational Studies Some Key Terms Requirements in Design Interplay between Design and Analysis Key Steps in Design A Simplified Model A Broader View AVOIDANCE OF BIAS General Remarks Randomization Retrospective Adjustment for Bias Some More on Randomization More on Causality CONTROL OF HAPHAZARD VARIATION General Remarks Precision Improvement by Blocking Matched Pairs Randomized Block Design Partitioning Sums of Squares Retrospective Adjustment for Improving Precision Special Models of Error Variation SPECIALIZED BLOCKING TECHNIQUES Latin Squares Incomplete Block Designs Cross-Over Designs FACTORIAL EXPERIMENTS: BASIC IDEAS General Remarks Example Main Effects and Interactions Example: Continued Two-Level Factorial Systems Fractional Factorials Example FACTORIAL EXPERIMENTS: FURTHER DEVELOPMENTS General Remarks Confounding in 2k Designs Other Factorial Systems Split Plot Designs Nonspecific Factors Designs for Quantitative Factors Taguchi Methods Conclusion OPTIMAL DESIGN General Remarks Some Simple Examples Some General Theory Other Optimality Criteria Algorithms for Design Construction Nonlinear Design Space-Filling Designs Bayesian Design Optimality of Traditional Designs SOME ADDITIONAL TOPICS Scale of Effort Adaptive Designs Sequential Regression Design Designs for One-Dimensional Error Structure Spatial Designs APPENDIX A: Statistical Analysis APPENDIX B: Some Algebra APPENDIX C: Computational Issues Each chapter also contains Bibliographic Notes plus Further Results and Exercises

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Abstract: In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html .

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Abstract: In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-Seq data, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data. DESeq2 uses shrinkage estimation for dispersions and fold changes to improve stability and interpretability of the estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression and facilitates downstream tasks such as gene ranking and visualization. DESeq2 is available as an R/Bioconductor package.

Machine Learning : A Probabilistic Perspective

Abstract: Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Modern Applied Statistics With S

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Approximate inference in generalized linear mixed models

Abstract: Statistical approaches to overdispersion, correlated errors, shrinkage estimation, and smoothing of regression relationships may be encompassed within the framework of the generalized linear mixed model (GLMM). Given an unobserved vector of random effects, observations are assumed to be conditionally independent with means that depend on the linear predictor through a specified link function and conditional variances that are specified by a variance function, known prior weights and a scale factor. The random effects are assumed to be normally distributed with mean zero and dispersion matrix depending on unknown variance components. For problems involving time series, spatial aggregation and smoothing, the dispersion may be specified in terms of a rank deficient inverse covariance matrix. Approximation of the marginal quasi-likelihood using Laplace's method leads eventually to estimating equations based on penalized quasilikelihood or PQL for the mean parameters and pseudo-likelihood for the variances. Im...