IDEAS home Printed from https://ideas.repec.org/f/pko700.html
   My authors  Follow this author

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

Moscow, Russia
http://lsa.hse.ru/

: +7(495)7713232
+7(495)6287931
Myasnitskaya 20, Moscow 101000
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. & 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. 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.

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. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Gaussian approximation of suprema of empirical processes," CeMMAP working papers CWP75/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Hardle, W. & Mammen, E., 1990. "Comparing nonparametric versus parametric regression fits," CORE Discussion Papers 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. Rob Euwals & Bertrand Melenberg & Arthur van Soest, 1998. "Testing the predictive value of subjective labour supply data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(5), pages 567-585.
    6. 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.
    7. 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.
    8. 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.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Valentin Konakov should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.