World's Best Scientists 2026 revealed!
Clifford M. Hurvich

Clifford M. Hurvich

D-Index & Metrics

Mathematics

D-Index
36
Citations
15627
World Ranking
2567
National Ranking
1062

Clifford M. Hurvich 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 Clifford M. Hurvich 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: 97 publications — 9th percentile

9% of scientists in this discipline score the same or lower.

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

Clifford M. Hurvich 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 Clifford M. Hurvich 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: 36 D-Index — 29th percentile

29% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2004 - Fellow of the American Statistical Association (ASA)

Overview

Clifford M. Hurvich is affiliated with New York University in the United States. Their research primarily focuses on fields related to Business, Management, and Accounting, with a concentration in areas such as Management Information Systems, Management Science and Operations Research, Finance, Marketing, and Industrial and Manufacturing Engineering.

Their main research topics cover Supply Chain and Inventory Management, Forecasting Techniques and Applications, Consumer Market Behavior and Pricing, Scheduling and Optimization Algorithms, Advanced Statistical Process Monitoring, Complex Systems and Time Series Analysis, and Statistical Methods and Inference.

Some notable recent papers include:

  • The propagation and identification of ARMA demand under simple exponential smoothing: forecasting expertise and information sharing (2020, IMA Journal of Management Mathematics)
  • Performance bound for myopic order-up-to inventory policies under stationary demand processes (2022, Operations Research Letters)
  • Partial Information Sharing in Supply Chains with ARMA Demand (2024, SSRN Electronic Journal)
  • Partial information sharing in supply chains with ARMA demand (2024, Naval Research Logistics (NRL))
  • Selection of Regression Models under Linear Restrictions for Fixed and Random Designs (2020, arXiv (Cornell University))

Hurvich's collaboration network includes frequent co-authors such as Avi Giloni, Vladimir Kovtun, René Caldentey, Sridhar Seshadri, and Meng-Chen Hsieh.

Their publications often appear in recognized venues, including:

  • SSRN Electronic Journal
  • arXiv (Cornell University)
  • Preprints.org
  • Mathematics of Operations Research
  • Econometrics and Statistics

In 2004, Hurvich was recognized as a Fellow of the American Statistical Association (ASA).

Best Publications

  • Regression and time series model selection in small samples

    Clifford M. Hurvich;Chih Ling Tsai

  • Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion

    Clifford M. Hurvich;Jeffrey S. Simonoff;Chih‐Ling Tsai

  • The mean squared error of Geweke and Porter‐Hudak's estimator of the memory parameter of a long‐memory time series

    Clifford M. Hurvich;Rohit Deo;Julia Brodsky

  • A CORRECTED AKAIKE INFORMATION CRITERION FOR VECTOR AUTOREGRESSIVE MODEL SELECTION

    Clifford M. Hurvich;Chih-Ling Tsai

  • Predictive Regressions: A Reduced-Bias Estimation Method

    Yakov Amihud;Clifford Hurvich

  • Bias of the corrected AIC criterion for underfitted regression and time series models

    Clifford M. Hurvich;Chih-Ling Tsai

  • The impact of model selection on inference in linear regression

    Clifford M. Hurvich;Chih-Ling Tsai

  • Model selection for extended quasi-likelihood models in small samples

    Clifford M. Hurvich;Chih-Ling Tsai

  • Forecasting realized volatility using a long-memory stochastic volatility model : estimation, prediction and seasonal adjustment

    Rohit Deo;Clifford Hurvich;Yi Lu

  • Multiple-Predictor Regressions: Hypothesis Testing

    Yakov Amihud;Clifford M. Hurvich;Yi Wang

  • ON THE LOG PERIODOGRAM REGRESSION ESTIMATOR OF THE MEMORY PARAMETER IN LONG MEMORY STOCHASTIC VOLATILITY MODELS

    Rohit S. Deo;Clifford M. Hurvich

  • ASYMPTOTICS FOR THE LOW‐FREQUENCY ORDINATES OF THE PERIODOGRAM OF A LONG‐MEMORY TIME SERIES

    Clifford M. Hurvich;Kaizo I. Beltrao

  • Estimating long memory in volatility

    Clifford M. Hurvich;Eric Moulines;Philippe Soulier

  • Improved estimators of Kullback-Leibler information for autoregressive model selection in small samples

    Clifford M. Hurvich;Robert Shumway;Chih-Ling Tsai

  • PLUG-IN SELECTION OF THE NUMBER OF FREQUENCIES IN REGRESSION ESTIMATES OF THE MEMORY PARAMETER OF A LONG-MEMORY TIME SERIES

    Clifford M. Hurvich;Rohit S. Deo

  • AUTOMATIC SEMIPARAMETRIC ESTIMATION OF THE MEMORY PARAMETER OF A LONG‐MEMORY TIME SERIES

    Clifford M. Hurvich;Kaizo I. Beltrao

  • An Efficient Taper for Potentially Overdifferenced Long-memory Time Series

    Clifford M. Hurvich;Willa W. Chen

  • Data-Driven Choice of a Spectrum Estimate: Extending the Applicability of Cross-Validation Methods

    Unknown

  • Semiparametric estimation of fractional cointegrating subspaces

    Willa W. Chen;Clifford M. Hurvich

  • The Local Whittle Estimator of Long-Memory Stochastic Volatility

    Clifford M. Hurvich;Bonnie K. Ray

  • Broadband Semiparametric Estimation of the Memory Parameter of a Long‐Memory Time Series Using Fractional Exponential Models

    Clifford M. Hurvich;Julia Brodsky

Frequent Co-Authors

Chih-Ling Tsai
Chih-Ling Tsai University of California, Davis
Jeffrey S. Simonoff
Jeffrey S. Simonoff New York University
Yakov Amihud
Yakov Amihud New York University
Lajos Horváth
Lajos Horváth University of Utah
Eric Moulines
Eric Moulines Mohamed bin Zayed University of Artificial Intelligence

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