Eileen Catherine King (born 1954) [1] is an American biostatistician specializing in the design and analysis of clinical trials. She is a professor in the Department of Pediatrics at the University of Cincinnati, in the Cincinnati Children's Hospital Medical Center. [2]
King graduated from Regis College (Massachusetts) in 1976. She earned a master's degree from the University of Wyoming in 1980, and completed a Ph.D. in 1988 at Texas A&M University. [2] Her dissertation, A test for the equality of two regression curves based on kernel smoothers, was supervised by Jeffrey D. Hart and Thomas Wehrly. [1] [3]
She joined the Cincinnati Children's Hospital Medical Center in 2009, after working in drug development for the pharmaceutical industry. [4]
King was the 2011 recipient of the H. O. Hartley Award of the Texas A&M University Department of Statistics, given to former students "for distinguished service to the discipline of statistics". [5] She was named a Fellow of the American Statistical Association in 2017. [6]
Sir David Roxbee Cox was a British statistician and educator. His wide-ranging contributions to the field of statistics included introducing logistic regression, the proportional hazards model and the Cox process, a point process named after him.
In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights. KDE answers a fundamental data smoothing problem where inferences about the population are made based on a finite data sample. In some fields such as signal processing and econometrics it is also termed the Parzen–Rosenblatt window method, after Emanuel Parzen and Murray Rosenblatt, who are usually credited with independently creating it in its current form. One of the famous applications of kernel density estimation is in estimating the class-conditional marginal densities of data when using a naive Bayes classifier, which can improve its prediction accuracy.
Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Its most common methods, initially developed for scatterplot smoothing, are LOESS and LOWESS, both pronounced LOH-ess. They are two strongly related non-parametric regression methods that combine multiple regression models in a k-nearest-neighbor-based meta-model. In some fields, LOESS is known and commonly referred to as Savitzky–Golay filter.
Nonparametric regression is a category of regression analysis in which the predictor does not take a predetermined form but is constructed according to information derived from the data. That is, no parametric form is assumed for the relationship between predictors and dependent variable. Nonparametric regression requires larger sample sizes than regression based on parametric models because the data must supply the model structure as well as the model estimates.
The term kernel is used in statistical analysis to refer to a window function. The term "kernel" has several distinct meanings in different branches of statistics.
In statistics, kernel regression is a non-parametric technique to estimate the conditional expectation of a random variable. The objective is to find a non-linear relation between a pair of random variables X and Y.
Herman Otto Hartley was a German American statistician. He made significant contributions in many areas of statistics, mathematical programming, and optimization. He also founded Texas A&M University's Department of Statistics.
Peter James Green, FRS is a British Bayesian statistician. He is emeritus Professor and Professorial Research Fellow at the University of Bristol, and a professor at the University of Technology, Sydney. He is distinguished for his contributions to computational statistics, in particular his contributions to spatial statistics and semi-parametric regression models and also his development of reversible-jump Markov chain Monte Carlo.
Robert Tibshirani is a professor in the Departments of Statistics and Biomedical Data Science at Stanford University. He was a professor at the University of Toronto from 1985 to 1998. In his work, he develops statistical tools for the analysis of complex datasets, most recently in genomics and proteomics.
Pranab Kumar Sen was an Indian-American statistician who was a professor of statistics and the Cary C. Boshamer Professor of Biostatistics at the University of North Carolina at Chapel Hill.
Susan Allbritton Murphy is an American statistician, known for her work applying statistical methods to clinical trials of treatments for chronic and relapsing medical conditions. She is a professor at Harvard University, a MacArthur Fellow, and a member of the National Academy of Sciences.
William Swain Cleveland II is an American computer scientist and Professor of Statistics and Professor of Computer Science at Purdue University, known for his work on data visualization, particularly on nonparametric regression and local regression. He is remembered as one of the developers of the S programming language.
Naomi Altman is a statistician known for her work on kernel smoothing[KS] and kernel regression,[KR] and interested in applications of statistics to gene expression and genomics. She is a professor of statistics at Pennsylvania State University, and a regular columnist for the "Points of Significance" column in Nature Methods.
Olga J. Pendleton is an American statistician known for her research on road traffic safety and alcohol-impaired driving as a statistician at the Texas A&M Transportation Institute, and as a member of the "Zero Alcohol" committee of the National Research Council. She has also published highly-cited work on the geometric design of roads and, with Ronald R. Hocking, on multiple linear regression.
Joan Georgette Staniswalis was an American statistician who made "significant contributions to theory and biomedical applications" of statistics, including the effects of air quality and racial inequality on health.
James E. Gentle is an American statistician and author. He was a professor of statistics at George Mason University until his retirement in 2016. He is Co-Editor-in-Chief of Wiley Interdisciplinary Reviews: Computational Statistics and Senior Editor of Communications in Statistics.
Lori Elizabeth Dodd is an American mathematical statistician specializing in clinical trials methodology, statistical analysis of genomic data, design of clinical trials using biomarkers and imaging modalities, and statistical methods for analyzing biomarkers. She is a statistician in the biostatistics research branch at the National Institute of Allergy and Infectious Diseases.
Babette Anne Brumback is an American biostatistician known for her work on causal inference. She is a professor of biostatistics at the University of Florida.
Simon J. Sheather is an Australian-American academic. He became the 8th dean of the University of Kentucky’s Gatton College of Business and Economics on July 1st, 2018. A Fellow of the American Statistical Association, Sheather is known for the Sheather-Jones bandwidth selection method for kernel density estimation.