World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
35
Citations
6528
World Ranking
2738
National Ranking
167

Alois Kneip publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Alois Kneip sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 91 publications — 7th percentile

7% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Alois Kneip D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Alois Kneip sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 35 D-Index — 25th percentile

25% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

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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