What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Internal medicine
His primary scientific interests are in Estimator, Statistics, Econometrics, Moment and Monte Carlo method.
When carried out as part of a general Estimator research project, his work on Generalized method of moments is frequently linked to work in Of the form, therefore connecting diverse disciplines of study.
His Econometrics research is multidisciplinary, incorporating elements of Latent class model, Statistical hypothesis testing, Count data and Causality.
His Moment research is multidisciplinary, relying on both Sample variance and Delta method.
His work carried out in the field of Sample variance brings together such families of science as Method of moments and Standard error.
His Monte Carlo method study frequently involves adjacent topics like Consistent estimator.
His most cited work include:
- A finite sample correction for the variance of linear efficient two-step GMM estimators (3248 citations)
- An R-squared measure of goodness of fit for some common nonlinear regression models (592 citations)
- Estimation in dynamic panel data models: Improving on the performance of the standard GMM estimator (562 citations)
What are the main themes of his work throughout his whole career to date?
Frank Windmeijer mainly investigates Estimator, Econometrics, Statistics, Instrumental variable and Panel data.
The Estimator study combines topics in areas such as Method of moments, Monte Carlo method, Moment and Applied mathematics.
Frank Windmeijer focuses mostly in the field of Monte Carlo method, narrowing it down to matters related to Delta method and, in some cases, Sample variance.
Frank Windmeijer has researched Econometrics in several fields, including Statistical hypothesis testing, Count data and Regression.
His study in the field of Goodness of fit, Linear model and First-difference estimator also crosses realms of Measure and Binary number.
He interconnects Observational study, Mendelian randomization and Causal inference in the investigation of issues within Instrumental variable.
He most often published in these fields:
- Estimator (35.78%)
- Econometrics (32.11%)
- Statistics (27.06%)
What were the highlights of his more recent work (between 2014-2021)?
- Instrumental variable (24.77%)
- Estimator (35.78%)
- Statistics (27.06%)
In recent papers he was focusing on the following fields of study:
His main research concerns Instrumental variable, Estimator, Statistics, Econometrics and Mendelian randomization.
The various areas that he examines in his Instrumental variable study include Observational study, Biobank, Causal inference and Covariate.
In his works, Frank Windmeijer undertakes multidisciplinary study on Estimator and Selection.
In the subject of general Statistics, his work in Likelihood-ratio test, Index of dissimilarity and Statistical model is often linked to Measure and Species evenness, thereby combining diverse domains of study.
Frank Windmeijer has included themes like Statistical hypothesis testing and Simple linear regression in his Econometrics study.
His research in Mendelian randomization intersects with topics in Epidemiology, Mendelian Randomization Analysis and Confounding.
Between 2014 and 2021, his most popular works were:
- An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. (180 citations)
- A weak instrument [Formula: see text]-test in linear IV models with multiple endogenous variables. (122 citations)
- Peer effects in charitable giving: Evidence from the (running) field (100 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Internal medicine
Instrumental variable, Statistics, Mendelian randomization, Econometrics and Confounding are his primary areas of study.
The various areas that he examines in his Instrumental variable study include Observational study, Genetic variation, Causal inference and Covariate.
His Estimator, Invariant estimator, Efficient estimator and Bias of an estimator investigations are all subjects of Statistics research.
His work on Minimum-variance unbiased estimator, Trimmed estimator, Consistent estimator and Delta method as part of general Estimator research is frequently linked to Eigenvalues and eigenvectors, bridging the gap between disciplines.
His work investigates the relationship between Mendelian randomization and topics such as Mendelian Randomization Analysis that intersect with problems in Standard error, Ordinary least squares, Linear model and Management science.
The concepts of his Econometrics study are interwoven with issues in Statistical hypothesis testing, Index of dissimilarity and Likelihood-ratio test.
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