John H. Wolfe

Last updated


John H. Wolfe is the inventor of model-based clustering for continuous data. [1] [2] [3] Wolfe graduated with a B.A. in mathematics from Caltech and then went to graduate school in psychology at the University of California, Berkeley to work with Robert Tryon.

Around 1959, Paul Lazarsfeld visited Berkeley and gave a lecture on his latent class analysis, which fascinated Wolfe, and led him to start thinking about how one could do the same thing for continuous data. Wolfe's 1963 M.A. thesis [4] is a first, but ultimately failed attempt to do this. After graduating from Berkeley, Wolfe took a job with the US Navy in San Diego first as a computer programmer and then as an operations research analyst.

He continued his research on clustering and in 1965 he published the paper that invented model-based clustering. [5] [3] He used the mixture of multivariate normal distributions model, estimated it by maximum likelihood using a Newton-Raphson algorithm and gave the expression for the posterior probabilities of membership in each cluster. This paper also contains the first publicly available software for estimating the model, called NORMIX. This was extended and published in a journal by Wolfe (1970). [6]

After 1970, Wolfe worked on other topics, but model-based clustering grew rapidly. Articles on model-based clustering have garnered over 20,000 citations in scientific publications [7] , while two of the most widely used software packages to implement it (the mclust and flexmix R packages) have been downloaded over 14 million times. [8]

Related Research Articles

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.

The following outline is provided as an overview of and topical guide to statistics:

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.

In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs. Formally a mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the overall population. However, while problems associated with "mixture distributions" relate to deriving the properties of the overall population from those of the sub-populations, "mixture models" are used to make statistical inferences about the properties of the sub-populations given only observations on the pooled population, without sub-population identity information. Mixture models are used for clustering, under the name model-based clustering, and also for density estimation.

<span class="mw-page-title-main">ROOT</span> Data analysis software

ROOT is an object-oriented computer program and library developed by CERN. It was originally designed for particle physics data analysis and contains several features specific to the field, but it is also used in other applications such as astronomy and data mining. The latest minor release is 6.28, as of 2023-02-03.

Ordination or gradient analysis, in multivariate analysis, is a method complementary to data clustering, and used mainly in exploratory data analysis. In contrast to cluster analysis, ordination orders quantities in a latent space. In the ordination space, quantities that are near each other share attributes, and dissimilar objects are farther from each other. Such relationships between the objects, on each of several axes or latent variables, are then characterized numerically and/or graphically in a biplot.

In statistics, a latent class model (LCM) is a model for clustering multivariate discrete data. It assumes that the data arise from a mixture of discrete distributions, within each of which the variables are independent. It is called a latent class model because the class to which each data point belongs is unobserved, or latent.

<span class="mw-page-title-main">GeoDa</span> Free geovisualization and analysis software

GeoDa is a free software package that conducts spatial data analysis, geovisualization, spatial autocorrelation and spatial modeling.

In statistics, confirmatory factor analysis (CFA) is a special form of factor analysis, most commonly used in social science research. It is used to test whether measures of a construct are consistent with a researcher's understanding of the nature of that construct. As such, the objective of confirmatory factor analysis is to test whether the data fit a hypothesized measurement model. This hypothesized model is based on theory and/or previous analytic research. CFA was first developed by Jöreskog (1969) and has built upon and replaced older methods of analyzing construct validity such as the MTMM Matrix as described in Campbell & Fiske (1959).

In statistics, the restrictedmaximum likelihood (REML) approach is a particular form of maximum likelihood estimation that does not base estimates on a maximum likelihood fit of all the information, but instead uses a likelihood function calculated from a transformed set of data, so that nuisance parameters have no effect.

In statistics, a generalized estimating equation (GEE) is used to estimate the parameters of a generalized linear model with a possible unmeasured correlation between observations from different timepoints. Although some believe that Generalized estimating equations are robust in everything even with the wrong choice of working-correlation matrix, Generalized estimating equations are only robust to loss of consistency with the wrong choice.

Detrended correspondence analysis (DCA) is a multivariate statistical technique widely used by ecologists to find the main factors or gradients in large, species-rich but usually sparse data matrices that typify ecological community data. DCA is frequently used to suppress artifacts inherent in most other multivariate analyses when applied to gradient data.

Psychometric software is software that is used for psychometric analysis of data from tests, questionnaires, or inventories reflecting latent psychoeducational variables. While some psychometric analyses can be performed with standard statistical software like SPSS, most analyses require specialized tools.

The Unscrambler X is a commercial software product for multivariate data analysis, used for calibration of multivariate data which is often in the application of analytical data such as near infrared spectroscopy and Raman spectroscopy, and development of predictive models for use in real-time spectroscopic analysis of materials. The software was originally developed in 1986 by Harald Martens and later by CAMO Software.

<span class="mw-page-title-main">Jacqueline Meulman</span> Dutch statistician

Jacqueline Meulman is a Dutch statistician and professor emerita of Applied Statistics at the Mathematical Institute of Leiden University.

The following outline is provided as an overview of and topical guide to machine learning:

Paul D. McNicholas is an Irish-Canadian statistician. He is a professor and University Scholar in the Department of Mathematics and Statistics at McMaster University. In 2015, McNicholas was awarded the Tier 1 Canada Research Chair in Computational Statistics. McNicholas uses computational statistics techniques, and mixture models in particular, to gain insight into large and complex datasets. He is editor-in-chief of the Journal of Classification.

In statistics, cluster analysis is the algorithmic grouping of objects into homogeneous groups based on numerical measurements. Model-based clustering bases this on a statistical model for the data, usually a mixture model. This has several advantages, including a principled statistical basis for clustering, and ways to choose the number of clusters, to choose the best clustering model, to assess the uncertainty of the clustering, and to identify outliers that do not belong to any group.

References

  1. ↑ McNicholas, P.D. (2016). Mixture Model-Based Classification. Chapman & Hall/CRC Press. ISBN   9781482225662.
  2. ↑ McNicholas, P.D. (2016). "Model-based clustering". Journal of Classification. 33: 331–373. doi:10.1007/s00357-016-9211-9.
  3. 1 2 Bouveyron, C.; Celeux, G.; Murphy, T.B.; Raftery, A.E. (2019). "Section 2.8". Model-Based Clustering and Classification for Data Science: With Applications in R. Cambridge University Press. ISBN   9781108494205.
  4. ↑ Wolfe, J.H. (1963). Object cluster analysis of social areas, M.A. thesis. University of California, Berkeley.
  5. ↑ Wolfe, J.H. (1965). A computer program for the maximum-likelihood analysis of types. USNPRA Technical Bulletin 65-15 (Report). US Naval Pers. Res. Act., San Diego, CA.
  6. ↑ Wolfe, J.H. (1970). "Pattern clustering by multivariate mixture analysis". Multivariate behavioral research. 5: 329–350. doi:10.1207/s15327906mbr0503_6.
  7. ↑ Assessed by adding the citations to all articles with "model-based clustering" in the title enumerated by Google Scholar, accessed March 3, 2024
  8. ↑ https://www.datasciencemeta.com/rpackages, accessed March 3, 2024


This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.