Nicole A. Lazar

Bio: Nicole A. Lazar is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Statistical hypothesis testing & Bayesian probability. The author has an hindex of 12, co-authored 14 publications receiving 13569 citations. Previous affiliations of Nicole A. Lazar include University of Illinois at Chicago.

Statistical Analysis With Missing Data

Abstract: (2003). Statistical Analysis With Missing Data. Technometrics: Vol. 45, No. 4, pp. 364-365.

Maturation of Cognitive Processes From Late Childhood to Adulthood

Abstract: To characterize cognitive maturation through adolescence, processing speed, voluntary response suppression, and spatial working memory were measured in 8- to 30-year-old (N = 245) healthy participants using oculomotor tasks. Development progressed with a steep initial improvement in performance followed by stabilization in adolescence. Adult-level mature performance began at approximately 15, 14, and 19 years of age for processing speed, response inhibition, and working memory, respectively. Although processes developed independently, processing speed influenced the development of working memory whereas the development of response suppression and working memory were interdependent. These results indicate that processing speed, voluntary response suppression, and working memory mature through late childhood and into adolescence. How brain maturation specific to adolescence may support cognitive maturation is discussed.

Statistics of Extremes: Theory and Applications

Abstract: At 520 pages and with a title of Cognition and Chance: The Psychology of Probabilistic Reasoning, I had hoped that this book would deepen my understanding of how human beings reason probabilistically. My interest stems from teaching and practicing statistics, where I have learned to be wary of quantitative intuition. I think that this is a common feeling among statisticians, who as a group often directly experience the inherent limitations and biases of how human beings observe, process, and interpret quantitative information. We know, for example, that human beings tend to find patterns in data where none exist, are wonderfully adept at post hoc rationalization of results and outcomes, quickly leap from correlation to causation, are subject to many subtle biases, and so on. So an authoritative treatment of the psychology of probabilistic reasoning would be quite useful and could help us understand when to trust and when to question human intuition. For example, what types of quantitative reasoning is the human brain naturally better or worse at? When are we good intuitive probabilists and when are we bad? Do we know why? Thus in reading this book, I hoped to gain a better understanding of such issues, if for no other reason than to help me better know when to trust my own intuition. Unfortunately, this book did not live up to my expectations. As I discuss more fully later in this review, it disappointed in three specific ways. The first disappointment is that the amount of the text specifically devoted to probabilistic reasoning is relatively small compared to the book’s total length. At 520 pages and 12 chapters, I expected a fairly deep and thorough discussion of the book’s titled topic. Yet only Chapter 11 (“People as Intuitive Probabilists”) is devoted to the particular subject of probabilistic reasoning. Chapters 8–10 (“Estimation and Prediction,” “Perception of Covariation and Contingency,” and “Choice Under Uncertainty”) are also related, discussing other aspects of quantitative reasoning, but fully two-thirds of the book is devoted to topics that are largely general background material. In particular, Chapters 1–7 focus on topics such as a general history of the field of probability as it developed from games of chance; the various meanings, interpretations, and misinterpretations of the concepts of randomness and coincidence; an entire chapter explaining Bayes’ theorem; another chapter devoted to a discussion of various paradoxes (e.g., St. Petersburg, Simpson’s); and, a general exposition of the field of statistics. These chapters seem to have been written for a lay audience and are largely nonquantitative. Other than perhaps the first chapter, they are likely to be of only passing interest to someone with advanced statistical training. The second disappointment is that the exposition tends to be more broad than deep. A typical discussion in the later chapters is of the form “researcher A found this and researcher B found that, whereas researcher C found a contrary result,” with the results described in only the briefest and most general terms. Few, if any, specifics about the various research efforts are described, and little effort seems to have been made to discuss the results in anything more than a superficial manner. For example, in Chapter 8 the author writes, “When asked to observe a set of numbers and to estimate some measure of central tendency, such as its mean, people are able under some conditions to produce reasonably accurate estimates (Beach & Swensson, 1966; Edwards, 1967; C.R. Peterson & Beach, 1967), although systematic deviations from actual values have also been reported (N.H. Anderson, 1964; I.P. Levin, 1974, 1975)” (p. 284). Now, although I have chosen one of the more egregious examples of a singularly unhelpful “discussion,” the lack of detail here is not atypical of the book’s general tone and approach. As a result, the reader is often left without sufficient information to truly understand the strengths or limitations of the cited results or the author’s summary conclusion. In a related vein, although the author’s grasp of a very large body of material is quite impressive, the narration often feels more like a wandering discussion than a focused examination. Furthermore, although each chapter does have a summary section, as does the book, each of these is superficial, simply regurgitating various general discussions from each chapter in an even more general fashion. In fact, after 435 pages of text, the summary chapter for the entire book is less than two full pages long. The third disappointment—related to the second—is the failure of the text to go beyond lists and discussions of individual studies and provide the reader with a broader context in which to place the information. That is, on completion of the book, the reader is left with various categories of research study results generally summarized, but little to no information about what this means for the broader question of how humans reason quantitatively and whether or not there are theories or models that help explain, summarize, or synthesize the various study results into some larger framework of human probabilistic reasoning. For example, there are no charts or graphics or tabularizations anywhere in the book that provide the reader with an overview or taxonomy of the field of research. Similarly, there is no outline or description of how psychologists think about or summarize the observed phenomenon nor any real discussion about various theories that may exist to explain human quantitative or probabilistic reasoning. Nothing in the book provides a reader with any sort of “big picture” within which to understand how the various lengthy expositions fit. Criticism aside, in reading Cognition and Chance: The Psychology of Probabilistic Reasoning, I did expand my knowledge about probabilistic reasoning. My disappointment may be the result of unrealistic expectations on my part or perhaps insufficient editorial assistance by the publisher. On the positive side, the book does bring together many diverse sources and results on a host of topics. As such, it could serve as a useful starting point for a new researcher beginning a study of some aspect of quantitative reasoning.

Methods and Criteria for Model Selection

Abstract: Model selection is an important part of any statistical analysis and, indeed, is central to the pursuit of science in general. Many authors have examined the question of model selection from both frequentist and Bayesian perspectives, and many tools for selecting the “best model” have been suggested in the literature. This paper considers the various proposals from a Bayesian decision–theoretic perspective.

Missing data: Our view of the state of the art.

Abstract: Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.

Experimental Design and Data Analysis for Biologists

Abstract: An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software

Community detection in graphs

Abstract: The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

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.