What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Probability distribution
The scientist’s investigation covers issues in Statistics, Birnbaum–Saunders distribution, Econometrics, Applied mathematics and Kurtosis.
His Statistics study frequently draws connections to other fields, such as Generalized function.
Víctor Leiva has researched Birnbaum–Saunders distribution in several fields, including Monte Carlo method, Inference and Calculus.
His Econometrics study which covers Distribution that intersects with T distribution.
His studies deal with areas such as Hazard, Kernel density estimation, Kernel method and Distribution as well as Applied mathematics.
His Kurtosis research integrates issues from Nonparametric statistics, Producer's risk, Acceptance sampling, Kernel embedding of distributions and Variable kernel density estimation.
His most cited work include:
- Acceptance sampling plans from truncated life tests based on the generalized birnbaum-saunders distribution (196 citations)
- Influence diagnostics in log-Birnbaum-Saunders regression models with censored data (107 citations)
- An R Package for a General Class of Inverse Gaussian Distributions (103 citations)
What are the main themes of his work throughout his whole career to date?
Víctor Leiva mostly deals with Statistics, Birnbaum–Saunders distribution, Econometrics, Monte Carlo method and Applied mathematics.
His is involved in several facets of Statistics study, as is seen by his studies on Regression analysis, Estimator, Maximum likelihood, Goodness of fit and Skewness.
His Regression analysis research incorporates themes from Quantile, Statistical model and Regression.
His Birnbaum–Saunders distribution research is multidisciplinary, incorporating perspectives in Outlier, Inference, Residual and Kurtosis.
His study looks at the relationship between Econometrics and fields such as Expectation–maximization algorithm, as well as how they intersect with chemical problems.
His Applied mathematics study combines topics in areas such as Probability density function, Distribution, Statistical inference, Mathematical optimization and Normal distribution.
He most often published in these fields:
- Statistics (49.71%)
- Birnbaum–Saunders distribution (34.29%)
- Econometrics (29.14%)
What were the highlights of his more recent work (between 2019-2021)?
- R software (12.00%)
- Statistics (49.71%)
- Monte Carlo method (28.00%)
In recent papers he was focusing on the following fields of study:
Víctor Leiva spends much of his time researching R software, Statistics, Monte Carlo method, Quantile regression and Econometrics.
In his study, Scientific evidence is strongly linked to Analytics, which falls under the umbrella field of Statistics.
The concepts of his Monte Carlo method study are interwoven with issues in Beta distribution, Applied mathematics and Normal distribution.
His work in the fields of Econometrics, such as Econometric model, overlaps with other areas such as Pandemic, Secondary sector of the economy and Factorial model.
His Distribution research includes elements of Skewness, Generalization and Inference.
His Regression analysis research incorporates elements of Estimator, Data set and Regression.
Between 2019 and 2021, his most popular works were:
- Econometric modeling of productivity and technical efficiency in the Chilean manufacturing industry (14 citations)
- Birnbaum‐Saunders quantile regression and its diagnostics with application to economic data (12 citations)
- Birnbaum-Saunders Quantile Regression Models with Application to Spatial Data (12 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Probability distribution
Víctor Leiva mainly investigates Statistics, Analytics, Quantile regression, Data set and R software.
While working in this field, he studies both Statistics and Wind speed.
Víctor Leiva usually deals with Quantile regression and limits it to topics linked to Data analysis and Spatial analysis, Marginal distribution, Distribution, Quantile and Multivariate statistics.
His Data set research is multidisciplinary, incorporating perspectives in Covariate, Regression, Estimator, Regression analysis and Robustness.
He integrates Particulates with Birnbaum–Saunders distribution in his study.
His Birnbaum–Saunders distribution research is multidisciplinary, relying on both Estimation theory, Errors-in-variables models and Residual.
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