What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Mathematical analysis
His primary scientific interests are in Asymptotic distribution, Statistics, Statistical hypothesis testing, Likelihood-ratio test and Likelihood function.
Herman Chernoff combines subjects such as Mathematical analysis, Asymptotic analysis, Order statistic and Sampling distribution with his study of Asymptotic distribution.
His is involved in several facets of Statistics study, as is seen by his studies on Normal distribution, Random variable and Probability distribution.
His Normal distribution research is multidisciplinary, incorporating elements of Illustration of the central limit theorem and Cumulative distribution function, Q-function.
The study of Likelihood-ratio test is intertwined with the study of Combinatorics in a number of ways.
His biological study spans a wide range of topics, including Goodness of fit, Test statistic, Restricted maximum likelihood and Score test.
His most cited work include:
- A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations (2970 citations)
- The Use of Faces to Represent Points in k- Dimensional Space Graphically (1063 citations)
- On the Distribution of the Likelihood Ratio (598 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Statistics, Applied mathematics, Mathematical optimization, Decision theory and Combinatorics.
His study in Likelihood-ratio test, Bayes' theorem, Asymptotic distribution, Random variable and Likelihood function falls within the category of Statistics.
His Score test study in the realm of Likelihood-ratio test connects with subjects such as Noncentral chi-squared distribution.
Design of experiments and Statistical hypothesis testing is closely connected to Optimal design in his research, which is encompassed under the umbrella topic of Mathematical optimization.
The Combinatorics study combines topics in areas such as Zero, Probability distribution, Illustration of the central limit theorem and Normal distribution.
The study incorporates disciplines such as Monte Carlo method and Mathematical analysis in addition to Normal distribution.
He most often published in these fields:
- Statistics (32.65%)
- Applied mathematics (15.31%)
- Mathematical optimization (12.24%)
What were the highlights of his more recent work (between 1999-2017)?
- Statistics (32.65%)
- Variables (4.08%)
- Machine learning (4.08%)
In recent papers he was focusing on the following fields of study:
His main research concerns Statistics, Variables, Machine learning, Artificial intelligence and Clustering high-dimensional data.
His work in Sample size determination and Fisher's exact test are all subfields of Statistics research.
His Fisher's exact test study combines topics in areas such as Yates's correction for continuity, Applied mathematics, Table and Bayes' theorem.
In his work, Data set and Word error rate is strongly intertwined with Feature selection, which is a subfield of Variables.
His work is dedicated to discovering how Machine learning, Measure are connected with Statistical theory, Algorithm, Upper and lower bounds and Sample and other disciplines.
His Artificial intelligence research is multidisciplinary, relying on both Partition and Trend analysis.
Between 1999 and 2017, his most popular works were:
- Why significant variables aren’t automatically good predictors (120 citations)
- Growth factor induced fibroblast differentiation from human bone marrow stromal cells in vitro (68 citations)
- Discovering influential variables: A method of partitions (28 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Mathematical analysis
Herman Chernoff focuses on Clustering high-dimensional data, Precision medicine, Gene regulatory network, Estrogen receptor alpha and Single-nucleotide polymorphism.
His Clustering high-dimensional data research incorporates Haystack, Feature selection, Machine learning, Trend analysis and Artificial intelligence.
His Precision medicine research is multidisciplinary, incorporating perspectives in Selection, Personalized medicine, Econometrics and Word error rate.
His research integrates issues of Statistical significance and Data set in his study of Word error rate.
His Gene regulatory network research incorporates a variety of disciplines, including Gene, Genetic marker, Case-control study, Genetics and Cancer.
Herman Chernoff integrates Estrogen receptor alpha and Candidate gene in his studies.
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