What is he best known for?
The fields of study he is best known for:
- Statistics
- Computer network
- Artificial intelligence
His scientific interests lie mostly in Local regression, Statistics, Smoothing, Polynomial regression and Nonparametric regression.
William S. Cleveland performs multidisciplinary study in the fields of Statistics and Environmental science via his papers.
He focuses mostly in the field of Smoothing, narrowing it down to matters related to Econometrics and, in some cases, Parametric family.
The concepts of his Polynomial regression study are interwoven with issues in Robust regression and Algorithm.
William S. Cleveland studied Robust regression and Scatterplot smoothing that intersect with Computer graphics and Decoding methods.
William S. Cleveland has researched Nonparametric regression in several fields, including Mathematical optimization and Parametric statistics.
His most cited work include:
- Robust Locally Weighted Regression and Smoothing Scatterplots (8024 citations)
- Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting (4017 citations)
- Visualizing Data (1649 citations)
What are the main themes of his work throughout his whole career to date?
Statistics, Data visualization, Visualization, Graphics and Statistical graphics are his primary areas of study.
His Statistics research incorporates themes from Seasonal adjustment and Applied mathematics.
His Color graphics and Interactive graphics study in the realm of Graphics connects with subjects such as Art history.
His studies examine the connections between Smoothing and genetics, as well as such issues in Nonparametric regression, with regards to Parametric statistics and Mathematical optimization.
Local regression is a primary field of his research addressed under Polynomial regression.
His work in Polynomial regression tackles topics such as Robust regression which are related to areas like Algorithm.
He most often published in these fields:
- Statistics (17.95%)
- Data visualization (12.82%)
- Visualization (11.97%)
What were the highlights of his more recent work (between 2008-2021)?
- Data visualization (12.82%)
- Data mining (11.11%)
- Complex data type (5.13%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Data visualization, Data mining, Complex data type, Visual analytics and Visualization.
The various areas that he examines in his Data visualization study include Server, Header and Drill down.
His research investigates the connection with Data mining and areas like Statistical model which intersect with concerns in Network packet and Choropleth map.
His work deals with themes such as Data modeling and Machine learning, which intersect with Visual analytics.
His study looks at the intersection of Visualization and topics like Trellis with Conditional dependence and Data management.
William S. Cleveland carries out multidisciplinary research, doing studies in Theoretical computer science and Embarrassingly parallel.
Between 2008 and 2021, his most popular works were:
- Local Regression Models (307 citations)
- A Visual Analytics Approach to Understanding Spatiotemporal Hotspots (96 citations)
- Large complex data: divide and recombine (D&R) with RHIPE (76 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Computer network
- Artificial intelligence
William S. Cleveland mainly investigates Data visualization, Visualization, Data mining, Visual analytics and Data science.
William S. Cleveland interconnects Distributed computing, Header and Server in the investigation of issues within Data visualization.
His Visualization research includes themes of Sampling, Computer graphics, Sample and Database.
His Data mining study incorporates themes from Statistical model and Choropleth map.
His Visual analytics study also includes
- Data modeling which is related to area like Computer graphics, Synthetic data and Geovisualization,
- Data set that connect with fields like Machine learning and Data exploration.
His Data science study integrates concerns from other disciplines, such as Outbreak and Medical emergency.
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