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Valentin Konakov

Personal Details

First Name:Valentin
Middle Name:
Last Name:Konakov
Suffix:
RePEc Short-ID:pko700
[This author has chosen not to make the email address public]
http://www.hse.ru/org/persons/22565341

Affiliation

International Laboratory of Stochastic Analysis
National Research University Higher School of Economics (HSE)

Moscow, Russia
http://lsa.hse.ru/
RePEc:edi:sahseru (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Konakov, V. & Mammen, Enno, 1996. "The Shape of Kernel Density Estimates in Higher Dimensions," SFB 373 Discussion Papers 1996,41, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  2. Konakov, V. & Läuter, H. & Liero, H., 1995. "Comparison of the Asymptotic Power of Tests Based on L. - and L.- Norms under Non-Standard Local Alternatives," SFB 373 Discussion Papers 1995,10, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  3. Konakov, V. & Läuter, H. & Liero, H., 1995. "Nonparametric versus Parametric Goodness of Fit," SFB 373 Discussion Papers 1995,49, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

Articles

  1. Konakov, Valentin & Mammen, Enno, 2001. "Local approximations of Markov random walks by diffusions," Stochastic Processes and their Applications, Elsevier, vol. 96(1), pages 73-98, November.
  2. Konakov, V. D. & Piterbarg, V. I., 1984. "On the convergence rate of maximal deviation distribution for kernel regression estimates," Journal of Multivariate Analysis, Elsevier, vol. 15(3), pages 279-294, December.
  3. Konakov, V. D., 1973. "Asymptotic properties of some functions of nonparametric estimates of a density function," Journal of Multivariate Analysis, Elsevier, vol. 3(4), pages 454-468, December.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Konakov, V. & Mammen, Enno, 1996. "The Shape of Kernel Density Estimates in Higher Dimensions," SFB 373 Discussion Papers 1996,41, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. José E. Chacón, 2020. "The Modal Age of Statistics," International Statistical Review, International Statistical Institute, vol. 88(1), pages 122-141, April.

  2. Konakov, V. & Läuter, H. & Liero, H., 1995. "Nonparametric versus Parametric Goodness of Fit," SFB 373 Discussion Papers 1995,49, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Neumann, Michael H., 1997. "Strong approximation of density estimators from weakly dependent observations by density estimators from independent observations," SFB 373 Discussion Papers 1997,86, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    3. Läuter, Henning & Sachsenweger, Cornelia, 1999. "Comparison of nonparametric goodness of fit tests," SFB 373 Discussion Papers 1999,2, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    4. Lee, Sokbae & Song, Kyungchul & Whang, Yoon-Jae, 2013. "Testing functional inequalities," Journal of Econometrics, Elsevier, vol. 172(1), pages 14-32.
    5. Läuter, Henning & Nikulin, Michail, 1999. "Parametric versus nonparametric goodness of fit: Another view," SFB 373 Discussion Papers 1999,14, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    6. Liero, Hannelore, 2001. "L2-tests for sparse multinomials," Statistics & Probability Letters, Elsevier, vol. 55(2), pages 147-158, November.

Articles

  1. Konakov, Valentin & Mammen, Enno, 2001. "Local approximations of Markov random walks by diffusions," Stochastic Processes and their Applications, Elsevier, vol. 96(1), pages 73-98, November.

    Cited by:

    1. Konakov Valentin & Mammen Enno, 2002. "Edgeworth type expansions for Euler schemes for stochastic differential equations," Monte Carlo Methods and Applications, De Gruyter, vol. 8(3), pages 271-286, December.

  2. Konakov, V. D. & Piterbarg, V. I., 1984. "On the convergence rate of maximal deviation distribution for kernel regression estimates," Journal of Multivariate Analysis, Elsevier, vol. 15(3), pages 279-294, December.

    Cited by:

    1. Sokbae Lee & Ryo Okui & Yoon†Jae Whang, 2017. "Doubly robust uniform confidence band for the conditional average treatment effect function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1207-1225, November.
    2. Ali Al-Sharadqah & Majid Mojirsheibani & William Pouliot, 2020. "On the performance of weighted bootstrapped kernel deconvolution density estimators," Statistical Papers, Springer, vol. 61(4), pages 1773-1798, August.
    3. Hardle, W. & Mammen, E., 1990. "Comparing nonparametric versus parametric regression fits," LIDAM Discussion Papers CORE 1990065, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Katharina Proksch, 2016. "On confidence bands for multivariate nonparametric regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 68(1), pages 209-236, February.
    5. Euwals, R.W. & Melenberg, B. & van Soest, A.H.O., 1997. "Testing the Predicitive Value of Subjective Labour Supply Data," Other publications TiSEM 84746c09-c5a3-46ba-a5e8-5, Tilburg University, School of Economics and Management.
    6. Paul Deheuvels & David Mason, 2004. "General Asymptotic Confidence Bands Based on Kernel-type Function Estimators," Statistical Inference for Stochastic Processes, Springer, vol. 7(3), pages 225-277, October.
    7. Mojirsheibani, Majid, 2012. "A weighted bootstrap approximation of the maximal deviation of kernel density estimates over general compact sets," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 230-241.
    8. Mojirsheibani, Majid, 2021. "A note on the performance of bootstrap kernel density estimation with small re-sample sizes," Statistics & Probability Letters, Elsevier, vol. 178(C).
    9. Majid Mojirsheibani, 2022. "On the maximal deviation of kernel regression estimators with NMAR response variables," Statistical Papers, Springer, vol. 63(5), pages 1677-1705, October.
    10. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Gaussian approximation of suprema of empirical processes," CeMMAP working papers CWP41/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Néstor Aguilera & Liliana Forzani & Pedro Morin, 2011. "On uniform consistent estimators for convex regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(4), pages 897-908.
    12. Ali Al-Sharadqah & Majid Mojirsheibani, 2020. "A simple approach to construct confidence bands for a regression function with incomplete data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(1), pages 81-99, March.
    13. Qiao, Wanli, 2021. "Extremes of locally stationary Gaussian and chi fields on manifolds," Stochastic Processes and their Applications, Elsevier, vol. 133(C), pages 166-192.

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