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Mathematics

D-Index
35
Citations
6528
World Ranking
2737
National Ranking
167

Overview

Alois Kneip is affiliated with the University of Bonn in Germany and has contributed primarily to the fields of Mathematics and Economics, Econometrics and Finance. Their research work exhibits a strong focus on Statistics and Probability, contributing extensively to the advancement of Statistical Methods and Inference.

Their scholarly output includes notable papers such as:

  • INFERENCE IN DYNAMIC, NONPARAMETRIC MODELS OF PRODUCTION: CENTRAL LIMIT THEOREMS FOR MALMQUIST INDICES, 2020, Econometric Theory
  • On the optimal reconstruction of partially observed functional data, 2020, The Annals of Statistics

Outside of this, their recent publication record is enriched by collaborations reflected in coauthored works with active researchers including:

  • Dominik Liebl
  • Eleonora Arnone
  • Fabio Nobile
  • Laura M. Sangalli
  • Daniel Becker

They have contributed to a range of topical areas within their domain, such as:

  • Statistical Methods and Inference
  • Bayesian Methods and Mixture Models
  • Monetary Policy and Economic Impact
  • Advanced Statistical Methods and Models
  • Efficiency Analysis Using DEA
  • Economic Growth and Productivity
  • Spatial and Panel Data Analysis

Frequent publication venues for Alois Kneip include outlets such as:

  • arXiv (Cornell University)
  • Econometric Theory
  • Statistica Sinica
  • Journal of the American Statistical Association
  • The Annals of Statistics

Their research spans subfields like Artificial Intelligence in addition to core areas of statistics and econometrics, reflecting a multi-disciplinary approach. Contributions to the Bayesian framework highlight an engagement with probabilistic modeling techniques.

The specific investigations by Alois Kneip include methodological developments such as central limit theorems for production indices and optimal reconstruction techniques in functional data analysis. Their work discusses both theoretical properties and applied statistical modeling challenges.

Best Publications

  • A note on the convergence of nonparametric DEA estimators for production efficiency scores

    Alois Kneip;Byeong U. Park;Léopold Simar

  • Robust principal component analysis for functional data

    N. Locantore;J. S. Marron;D. G. Simpson;N. Tripoli

  • Asymptotics and Consistent Bootstraps for Dea Estimators in Nonparametric Frontier Models

    Alois Kneip;Léopold Simar;Paul W. Wilson

  • SMOOTHING SPLINES ESTIMATORS FOR FUNCTIONAL LINEAR REGRESSION

    Christophe Crambes;Alois Kneip;Pascal Sarda

  • A Flexible and Fast Method for Automatic Smoothing

    Theo Gasser;Alois Kneip;Walter Köhler

  • Statistical Tools to Analyze Data Representing a Sample of Curves

    Alois Kneip;Theo Gasser

  • Inference for Density Families Using Functional Principal Component Analysis

    Alois Kneip;Alois Kneip;Klaus J Utikal

  • Common functional principal components

    Michal Benko;Wolfgang Karl Härdle;Alois Kneip

  • Searching for Structure in Curve Samples

    Theo Gasser;Alois Kneip

  • A general framework for frontier estimation with panel data

    Alois Kneip;Léopold Simar

  • Combining Registration and Fitting for Functional Models

    Alois Kneip;James O Ramsay

  • Choice of bandwidth for kernel regression when residuals are correlated

    Eva Herrmann;Theo Gasser;Alois Kneip

  • Ordered Linear Smoothers

    Alois Kneip

  • Testing Hypotheses in Nonparametric Models of Production

    Alois Kneip;Léopold Simar;Paul W. Wilson

  • Velocity and acceleration of height growth using kernel estimation

    Gasser T;Köhler W;Müller Hg;Kneip A

  • Convergence and consistency results for self-modeling nonlinear regression

    Alois Kneip;Theo Gasser

  • A New Panel Data Treatment for Heterogeneity in Time Trends

    Alois Kneip;Robin Christopher Sickles;Wonho Song

  • On the estimation of jump points in smooth curves

    Irene Gijbels;Peter Hall;Peter Hall;Aloïs Kneip

  • When Bias Kills the Variance: Central Limit Theorems for DEA and FDH Efficiency Scores

    Alois Kneip;Léopold Simar;Paul W. Wilson

  • The dynamics of linear growth in distance, velocity and acceleration.

    T. Gasser;A. Kneip;A. Binding;A. Prader

  • Common Functional Principal Components

    Wolfgang K. Härdle;Alois Kneip

  • Smoothing spline models for the analysis of nested and crossed samples of curves - Comment

    Alois Kneip

Frequent Co-Authors

Paul W. Wilson
Paul W. Wilson Clemson University
Léopold Simar
Léopold Simar Université Catholique de Louvain
Hans-Georg Müller
Hans-Georg Müller University of California, Davis
James O. Ramsay
James O. Ramsay McGill University
Robin C. Sickles
Robin C. Sickles Rice University
Wolfgang Karl Härdle
Wolfgang Karl Härdle Humboldt-Universität zu Berlin
Lisa Feldman Barrett
Lisa Feldman Barrett Northeastern University
James Stephen Marron
James Stephen Marron University of North Carolina at Chapel Hill
Byeong U. Park
Byeong U. Park Seoul National University
Tor D. Wager
Tor D. Wager Dartmouth College

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