What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Mathematical analysis
His primary areas of investigation include Statistics, Kernel density estimation, Smoothing, Mathematical optimization and Estimator.
As part of his studies on Statistics, he often connects relevant subjects like Econometrics.
His Kernel density estimation study frequently draws parallels with other fields, such as Variable kernel density estimation.
His Smoothing research includes elements of Algorithm, Probability density function and Kernel method.
His Mathematical optimization study integrates concerns from other disciplines, such as Density estimation and Applied mathematics.
In his research on the topic of Estimator, Minimum distance estimation and Parametric statistics is strongly related with Divergence.
His most cited work include:
- A reliable data-based bandwidth selection method for kernel density estimation (1843 citations)
- A Brief Survey of Bandwidth Selection for Density Estimation (1023 citations)
- Spline Smoothing and Nonparametric Regression (1015 citations)
What are the main themes of his work throughout his whole career to date?
M. C. Jones spends much of his time researching Statistics, Kernel density estimation, Applied mathematics, Estimator and Mathematical optimization.
His study looks at the relationship between Statistics and topics such as Econometrics, which overlap with Parametric statistics.
His Kernel density estimation study combines topics from a wide range of disciplines, such as Smoothing, Density estimation and Multivariate kernel density estimation, Kernel method, Variable kernel density estimation.
The concepts of his Smoothing study are interwoven with issues in Algorithm and Probability density function.
His studies deal with areas such as Mathematical analysis, Exponential function, Random variable, Transformation and Calculus as well as Applied mathematics.
M. C. Jones combines Mathematical optimization and Bandwidth in his research.
He most often published in these fields:
- Statistics (34.03%)
- Kernel density estimation (28.80%)
- Applied mathematics (26.70%)
What were the highlights of his more recent work (between 2012-2021)?
- Applied mathematics (26.70%)
- Random variable (10.47%)
- Distribution (7.33%)
In recent papers he was focusing on the following fields of study:
M. C. Jones mostly deals with Applied mathematics, Random variable, Distribution, Pure mathematics and Mathematical analysis.
M. C. Jones has included themes like Estimator, Exponentiated Weibull distribution, Stability and Random variate in his Applied mathematics study.
His Random variable research is multidisciplinary, relying on both Uniform distribution, Quantile and Combinatorics.
He studied Distribution and Univariate that intersect with Inverse Gaussian distribution, Univariate distribution, Heavy-tailed distribution and Normal-gamma distribution.
His Jarque–Bera test study is focused on Statistics in general.
Statistics and Bathtub are frequently intertwined in his study.
Between 2012 and 2021, his most popular works were:
- On Families of Distributions with Shape Parameters (42 citations)
- Parametric quantile regression based on the generalized gamma distribution (34 citations)
- On a class of circulas: copulas for circular distributions (21 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Mathematical analysis
M. C. Jones mainly investigates Applied mathematics, Generalized gamma distribution, Stability, Estimator and Mathematical analysis.
His Applied mathematics research incorporates elements of Convolution, Kurtosis and Cardioid.
His studies in Stability integrate themes in fields like Divergence and Special case.
His Estimator research includes themes of Algorithm, Bounded function and Power transform.
His work in the fields of Mathematical analysis, such as Cauchy distribution, Trigonometry and Möbius transformation, overlaps with other areas such as Simple.
His Quantile study results in a more complete grasp of Statistics.
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