What is he best known for?
The fields of study he is best known for:
- Statistics
- Mathematical analysis
- Random variable
Richard A. Davis spends much of his time researching Statistics, Series, Applied mathematics, Point process and Stationary process.
His work carried out in the field of Series brings together such families of science as Segmentation, Genetic algorithm, Optimization problem and Computer simulation.
His Applied mathematics research includes themes of Likelihood-ratio test, Convergence of random variables, Ornstein–Uhlenbeck process, White noise and Null distribution.
His biological study spans a wide range of topics, including Function, Autocovariance and Autocorrelation.
His Autocovariance research is multidisciplinary, relying on both Cross-spectrum, Autoregressive fractionally integrated moving average, Order of integration and Lag operator.
His study in Autocorrelation is interdisciplinary in nature, drawing from both Stationary sequence, Econometrics and Time series.
His most cited work include:
- Time Series: Theory and Methods (5134 citations)
- Introduction to time series and forecasting (2466 citations)
- Extremes and Related Properties of Random Sequences and Processes. (1756 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Applied mathematics, Series, Statistics, Asymptotic distribution and Autoregressive model.
As part of one scientific family, Richard A. Davis deals mainly with the area of Applied mathematics, narrowing it down to issues related to the Mathematical optimization, and often Covariance and Gaussian process.
Richard A. Davis mostly deals with Autocovariance in his studies of Series.
His study explores the link between Autocovariance and topics such as Stationary process that cross with problems in Function and Combinatorics.
The various areas that he examines in his Asymptotic distribution study include Autocorrelation, Moving average and Mathematical analysis, Limit.
His Autoregressive model study incorporates themes from Autoregressive integrated moving average and STAR model.
He most often published in these fields:
- Applied mathematics (35.59%)
- Series (23.84%)
- Statistics (22.06%)
What were the highlights of his more recent work (between 2012-2021)?
- Applied mathematics (35.59%)
- Series (23.84%)
- Autoregressive model (17.08%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Applied mathematics, Series, Autoregressive model, Estimator and Mathematical optimization.
His Applied mathematics research includes themes of Covariance, Inference, Asymptotic distribution, Limit and Consistency.
Autocovariance is closely connected to Singular value in his research, which is encompassed under the umbrella topic of Limit.
The study incorporates disciplines such as Univariate, Asymptotic theory and Goodness of fit, Statistics, Markov chain in addition to Series.
He interconnects Autoregressive integrated moving average, Sparse vector and Noise in the investigation of issues within Autoregressive model.
His Estimator study also includes fields such as
- Degree which connect with Function,
- Power law and related Network model, Enhanced Data Rates for GSM Evolution and Parametric statistics.
Between 2012 and 2021, his most popular works were:
- Sparse Vector Autoregressive Modeling (110 citations)
- Statistical inference for max-stable processes in space and time (64 citations)
- Handbook of discrete-valued time series (46 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Mathematical analysis
- Random variable
Applied mathematics, Series, Autoregressive model, Statistical physics and Mathematical optimization are his primary areas of study.
He has researched Applied mathematics in several fields, including Parametric model, Inference, Asymptotic distribution, Limit and Isotropy.
Richard A. Davis studies Series, focusing on Autocovariance in particular.
His studies deal with areas such as Singular value, Pure mathematics, Cross-spectrum, Spectral density and Stationary process as well as Autocovariance.
His research integrates issues of Stationary sequence, Poisson point process, Gaussian process, Lattice and Covariance function in his study of Statistical physics.
His Mathematical optimization research is multidisciplinary, incorporating elements of Consistency, Algorithm, Likelihood function, Feature selection and Order of integration.
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