What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Random variable
David Siegmund mostly deals with Statistics, Applied mathematics, Brownian motion, Mathematical analysis and Statistical hypothesis testing.
David Siegmund undertakes multidisciplinary studies into Statistics and Variance in his work.
His biological study spans a wide range of topics, including Maximum likelihood, Asymptotic expansion, Queueing theory and Random walk.
His Queueing theory research focuses on Calculus and how it relates to Confidence interval.
His studies in Brownian motion integrate themes in fields like Martingale, Observed information, Discrete time and continuous time and Econometrics.
His study on Statistical hypothesis testing also encompasses disciplines like
- Monte Carlo method and related Random variable,
- Sequential estimation, which have a strong connection to Sample size determination.
His most cited work include:
- Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach (1113 citations)
- Great expectations: The theory of optimal stopping (723 citations)
- Sequential Analysis: Tests and Confidence Intervals (685 citations)
What are the main themes of his work throughout his whole career to date?
David Siegmund mainly investigates Statistics, Random variable, Applied mathematics, Statistic and Genetics.
In his study, Pedigree chart is inextricably linked to Quantitative trait locus, which falls within the broad field of Statistics.
His Random variable study also includes fields such as
- Sequence that intertwine with fields like Segmentation and Point,
- Combinatorics which is related to area like Independent and identically distributed random variables and Limit.
His Applied mathematics research incorporates elements of Normal distribution, Discrete time and continuous time, Random walk and Brownian motion.
His research in Brownian motion intersects with topics in Statistical physics and Mathematical analysis.
The concepts of his Statistic study are interwoven with issues in Algorithm, Generalization and Fraction.
He most often published in these fields:
- Statistics (33.09%)
- Random variable (17.27%)
- Applied mathematics (15.11%)
What were the highlights of his more recent work (between 2009-2021)?
- Statistics (33.09%)
- Statistic (11.51%)
- Random variable (17.27%)
In recent papers he was focusing on the following fields of study:
David Siegmund focuses on Statistics, Statistic, Random variable, Algorithm and Sequence.
His Statistics research is multidisciplinary, incorporating elements of Econometrics and False discovery rate.
His research investigates the link between Statistic and topics such as Fraction that cross with problems in Interval, Carry and Scan statistic.
His Algorithm research includes themes of Discrete mathematics, Change detection and Electronic engineering.
As part of one scientific family, he deals mainly with the area of Sequence, narrowing it down to issues related to the Segmentation, and often Point and Sample size determination.
Within one scientific family, David Siegmund focuses on topics pertaining to Limit under Random field, and may sometimes address concerns connected to Applied mathematics.
Between 2009 and 2021, his most popular works were:
- Sequential multi-sensor change-point detection (142 citations)
- Detecting simultaneous changepoints in multiple sequences. (125 citations)
- Detecting simultaneous variant intervals in aligned sequences (58 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Gene
His primary scientific interests are in Statistic, Fraction, Change detection, Algorithm and Sequence.
His Statistic study combines topics in areas such as Confidence bounds, Range, Sample size determination and Power function.
His Fraction research incorporates elements of Segmentation, Data stream mining, Applied mathematics, Mixture model and Generalization.
In his study, which falls under the umbrella issue of Segmentation, Statistics is strongly linked to Biometrics.
As part of his studies on Statistics, he often connects relevant subjects like Pattern recognition.
His Sequence research incorporates themes from Range, Variety and Constant false alarm rate.
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