Jacqueline Meulman (born 7 July 1954) is a Dutch statistician and professor emerita of Applied Statistics at the Mathematical Institute of Leiden University.
Jacqueline Meulman (born 7 July 1954) is a Dutch statistician and professor emerita of Applied Statistics at the Mathematical Institute of Leiden University.
Born in The Hague, Meulman received her master's degree in mathematical psychology and data theory at Leiden University in 1981, and obtained her PhD in data theory in 1986 with the thesis entitled "A distance approach to nonlinear multivariate analysis" advised by Jan de Leeuw and John P. van de Geer. [1] She was a consultant for Bell Telephone Laboratories in Murray Hill, NJ, from 1982 to 1983.
In addition to being an associate professor in the Department of Data Theory in Leiden, she was an adjunct professor at the University of Illinois at Urbana–Champaign from 1993 to 1999. In 1998, she was appointed Professor of Applied Data Theory at Leiden University. Since 2009 she is Professor of Applied Statistics at the Mathematical Institute in Leiden. She is currently also an adjunct professor at the Department of Statistics at Stanford University.
Meulman has received several awards, including a five-year "fellowship" of the Royal Netherlands Academy of Arts and Sciences in 1987, de J.C. Ruigrok prijs from the Royal Holland Society of Sciences and Humanities (KHMW) in 1991, a Fulbright Award in 1992, and a PIONEER grant from the Netherlands Organization of Scientific Research (NWO) in 1994. In 2001 she was elected President of the International Psychometric Society, and since 2002 she is a member of the Royal Netherlands Academy of Arts and Sciences (KNAW). [2] From 2011 to 2017 she was president of the Netherlands Society for Statistics and Operations Research. She is an Elected Member of the International Statistical Institute since 1996, and she was elected to the Royal Holland Society of Sciences and Humanities in 2015.
Meulman's research interest are in the field of statistics and data science, and her work includes the development of new statistical methods with applications in the life and behavioral sciences.
Since the 1990s, she manages the development of software in the package CATEGORIES of IBM SPSS Statistics that includes programs for optimal scaling in regularized multiple regression analysis, principal components analysis, correspondence analysis, multidimensional scaling and unfolding. [3] [4] At the Mathematical Institute in Leiden, she was Program Director of the first Dutch Master in Applied Statistics and Data Science in a Faculty of Science. [5] She is also one of the Founding Fathers and Co-Director of the Leiden Centre of Data Science (LCDS). [6]
Meulman has authored and co-authored many publications [7] in the field of statistical research and its applications.
Books, a selection:
Articles, a selection:
Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable, i.e., multivariate random variables. Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to each other. The practical application of multivariate statistics to a particular problem may involve several types of univariate and multivariate analyses in order to understand the relationships between variables and their relevance to the problem being studied.
Psychological statistics is application of formulas, theorems, numbers and laws to psychology. Statistical methods for psychology include development and application statistical theory and methods for modeling psychological data. These methods include psychometrics, factor analysis, experimental designs, and Bayesian statistics. The article also discusses journals in the same field.
Statistics is a field of inquiry that studies the collection, analysis, interpretation, and presentation of data. It is applicable to a wide variety of academic disciplines, from the physical and social sciences to the humanities; it is also used and misused for making informed decisions in all areas of business and government.
Chemometrics is the science of extracting information from chemical systems by data-driven means. Chemometrics is inherently interdisciplinary, using methods frequently employed in core data-analytic disciplines such as multivariate statistics, applied mathematics, and computer science, in order to address problems in chemistry, biochemistry, medicine, biology and chemical engineering. In this way, it mirrors other interdisciplinary fields, such as psychometrics and econometrics.
Mathematical statistics is the application of probability theory, a branch of mathematics, to statistics, as opposed to techniques for collecting statistical data. Specific mathematical techniques which are used for this include mathematical analysis, linear algebra, stochastic analysis, differential equations, and measure theory.
Quantitative psychology is a field of scientific study that focuses on the mathematical modeling, research design and methodology, and statistical analysis of psychological processes. It includes tests and other devices for measuring cognitive abilities. Quantitative psychologists develop and analyze a wide variety of research methods, including those of psychometrics, a field concerned with the theory and technique of psychological measurement.
Louis (Eliyahu) Guttman was an American sociologist and Professor of Social and Psychological Assessment at the Hebrew University of Jerusalem, known primarily for his work in social statistics.
Chi-square automatic interaction detection (CHAID) is a decision tree technique based on adjusted significance testing. The technique was developed in South Africa and was published in 1980 by Gordon V. Kass, who had completed a PhD thesis on this topic. CHAID can be used for prediction as well as classification, and for detection of interaction between variables. CHAID is based on a formal extension of AID and THAID procedures of the 1960s and 1970s, which in turn were extensions of earlier research, including that performed by Belson in the UK in the 1950s. A history of earlier supervised tree methods together with a detailed description of the original CHAID algorithm and the exhaustive CHAID extension by Biggs, De Ville, and Suen, can be found in Ritschard.
The Unistat computer program is a statistical data analysis tool featuring two modes of operation: The stand-alone user interface is a complete workbench for data input, analysis and visualization while the Microsoft Excel add-in mode extends the features of the mainstream spreadsheet application with powerful analytical capabilities.
Repeated measures design is a research design that involves multiple measures of the same variable taken on the same or matched subjects either under different conditions or over two or more time periods. For instance, repeated measurements are collected in a longitudinal study in which change over time is assessed.
In multivariate statistics, exploratory factor analysis (EFA) is a statistical method used to uncover the underlying structure of a relatively large set of variables. EFA is a technique within factor analysis whose overarching goal is to identify the underlying relationships between measured variables. It is commonly used by researchers when developing a scale and serves to identify a set of latent constructs underlying a battery of measured variables. It should be used when the researcher has no a priori hypothesis about factors or patterns of measured variables. Measured variables are any one of several attributes of people that may be observed and measured. Examples of measured variables could be the physical height, weight, and pulse rate of a human being. Usually, researchers would have a large number of measured variables, which are assumed to be related to a smaller number of "unobserved" factors. Researchers must carefully consider the number of measured variables to include in the analysis. EFA procedures are more accurate when each factor is represented by multiple measured variables in the analysis.
Peter J. Rousseeuw is a statistician known for his work on robust statistics and cluster analysis. He obtained his PhD in 1981 at the Vrije Universiteit Brussel, following research carried out at the ETH in Zurich, which led to a book on influence functions. Later he was professor at the Delft University of Technology, The Netherlands, at the University of Fribourg, Switzerland, and at the University of Antwerp, Belgium. Next he was a senior researcher at Renaissance Technologies. He then returned to Belgium as professor at KU Leuven, until becoming emeritus in 2022. His former PhD students include Annick Leroy, Hendrik Lopuhaä, Geert Molenberghs, Christophe Croux, Mia Hubert, Stefan Van Aelst, Tim Verdonck and Jakob Raymaekers.
Jan de Leeuw is a Dutch statistician and psychometrician. He is distinguished professor emeritus of statistics and founding chair of the Department of Statistics, University of California, Los Angeles. In addition, he is the founding editor and former editor-in-chief of the Journal of Statistical Software, as well as the former editor-in-chief of the Journal of Multivariate Analysis and the Journal of Educational and Behavioral Statistics.
Willem Jan Heiser is a Dutch social scientist who was Professor of Psychology, Statistical Methods and Data Theory at the Leiden University between 1989 and 2014.
Johannes Petrus "John" van de Geer was a Dutch psychologist, and Professor of Experimental Psychology at Leiden University, particularly known for his "Introduction to multivariate analysis for the social sciences".
Patrick John Fitzgerald (Patrick) Groenen is a Dutch economist and Professor of Statistics at the Erasmus School of Economics (ESE) of the Erasmus University Rotterdam, known for his work in the fields of exploratory factor analysis, multidimensional scaling and numerical algorithms in these fields.
The growth curve model in statistics is a specific multivariate linear model, also known as GMANOVA. It generalizes MANOVA by allowing post-matrices, as seen in the definition.
Fang Kaitai, also known as Kai-Tai Fang, is a Chinese mathematician and statistician who has helped to develop generalized multivariate analysis, which extends classical multivariate analysis beyond the multivariate normal distribution to more general elliptical distributions. He has also contributed to the design of experiments.
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