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Statistica Sinica, ISSN 1017-0405, 2020
Journal Article
Opuscula Mathematica, ISSN 1232-9274, 01/2019, Volume 39, Issue 5, pp. 733 - 746
In this paper we prove large and moderate deviations principles for the recursive kernel estimators of a distribution function defined by the stochastic... 
distribution estimation | stochastic approximation algorithm | large and moderate deviations principles
Journal Article
Journal of Multivariate Analysis, ISSN 0047-259X, 09/2019, Volume 173, pp. 494 - 511
We propose and investigate a new kernel regression estimator based on the minimization of the mean squared relative error. We study the properties of the... 
Mean square relative error | Functional nonparametric statistics | Stochastic approximation algorithm | Asymptotic normality | Bootstrap | Nonparametric estimation | Functional data analysis | STOCHASTIC-APPROXIMATION METHOD | ERROR PREDICTION | STATISTICS & PROBABILITY | R-PACKAGE | Statistics
Journal Article
Statistics and Probability Letters, ISSN 0167-7152, 08/2019, Volume 151, pp. 17 - 28
In the present paper, we are mainly concerned with a family of kernel type estimators based upon spatial data. More precisely, we establish large and moderate... 
Large and moderate deviation principles | Stochastic approximation algorithm | Nonparametric regression | Statistics | Mathematics
Journal Article
Statistics and Probability Letters, ISSN 0167-7152, 08/2019, Volume 151, pp. 116 - 122
In this paper we define and study a new estimator of the regression function when the response random variable is subject to random right-censoring. The... 
Censored data | Relative regression | Mean squared relative error | Asymptotic normality | Consistency | Statistics
Journal Article
Journal of Probability and Statistics, ISSN 1687-952X, 2014, Volume 2014, pp. 1 - 11
  We propose an automatic selection of the bandwidth of the recursive kernel estimators of a probability density function defined by the stochastic... 
Economic models | Algorithms | Normal distribution | Work stations | Time series | Optimization | Methods | Kernels | Errors | Approximation | Mathematical analysis | Bandwidth | Stochasticity | Recursive | Estimators | Statistics
Journal Article
Statistics and Probability Letters, ISSN 0167-7152, 08/2018, Volume 139, pp. 103 - 114
In the present paper we propose recursive general kernel-type estimators for spatial data defined by the stochastic approximation algorithm. We obtain the... 
Bandwidth selection | Stochastic approximation algorithm | Spatial data | Regression estimation | Algorithms | Geospatial data | Analysis | Methods | Probability | Mathematics | Functional Analysis | Statistics | Statistics Theory
Journal Article
Journal of Statistical Theory and Practice, ISSN 1559-8608, 10/2016, Volume 10, Issue 4, pp. 656 - 672
In this article we propose an automatic selection of the bandwidth of the semirecursive kernel estimators of the hazard function for uncensored observations.... 
stochastic approximation algorithm | curve fitting | smoothing | Primary 62G05, 62L20 | Secondary 65D10 | Nonparametric hazard estimation | Statistical Theory and Methods | Probability Theory and Stochastic Processes | Statistics, general | Statistics
Journal Article
Statistica Neerlandica, ISSN 0039-0402, 11/2015, Volume 69, Issue 4, pp. 483 - 509
Journal Article
Communications in Statistics - Theory and Methods, ISSN 0361-0926, 03/2019, pp. 1 - 22
Journal Article
Journal of Nonparametric Statistics, ISSN 1048-5252, 04/2018, Volume 30, Issue 2, pp. 505 - 522
In this paper, we propose two kernel density estimators based on a bias reduction technique. We study the properties of these estimators and compare them with... 
Density estimation | bias reduction | Primary: 62G07; 62L20 | Secondary: 65D10 | stochastic approximation algorithm | curve fitting | smoothing | STATISTICS & PROBABILITY | REGULARLY VARYING SEQUENCES | PROBABILITY DENSITY | 62L20 | Primary: 62G07 | Economic models | Reduction | Parameter estimation | Bias | Estimating techniques | Probabilistic inference | Density | Estimators | Statistics
Journal Article
Communications in Statistics - Theory and Methods, ISSN 0361-0926, 09/2017, Volume 46, Issue 18, pp. 9101 - 9125
Journal Article
JOURNAL OF THE JAPAN STATISTICAL SOCIETY, ISSN 1882-2754, 2016, Volume 46, Issue 1, pp. 1 - 26
In this paper we consider the kernel estimators of a distribution function defined by the stochastic approximation algorithm when the observation are... 
deconvolution | distribution estimation | plug-in methods | stochastic approximation algorithm | Bandwidth selection | Kernels | Bandwidth
Journal Article
Statistica Neerlandica, ISSN 0039-0402, 11/2015, Volume 69, Issue 4, pp. 483 - 509
Journal Article
Journal of Statistical Theory and Practice, ISSN 1559-8608, 6/2019, Volume 13, Issue 2, pp. 1 - 21
In this paper, we extend the work of Slaoui (J Probab Stat, https://doi.org/10.1155/2014/739640, 2014) to the case of strong mixing data. Then, we study the... 
62G05 | Density estimation | 62G07 | Bandwidth selection | Stochastic approximation algorithm | 62G08 | Statistical Theory and Methods | Probability Theory and Stochastic Processes | Statistics, general | Statistics | Asymptotic normality | Recursive kernel estimators | 62G20 | 62H12 | Mixing data
Journal Article
Statistics and its Interface, ISSN 1938-7989, 2016, Volume 9, Issue 3, pp. 375 - 388
In this paper we propose an automatic selection of the bandwidth of the semi-recursive kernel estimators of a regression function defined by the stochastic... 
Stochastic approximation algorithm | Smoothing | Curve fitting | Nonparametric regression | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | STOCHASTIC-APPROXIMATION METHOD | DENSITY-ESTIMATION | MATHEMATICAL & COMPUTATIONAL BIOLOGY | REGULARLY VARYING SEQUENCES
Journal Article
Journal of Nonparametric Statistics, ISSN 1048-5252, 10/2017, Volume 29, Issue 4, pp. 792 - 805
We propose a recursive distribution estimator using Robbins-Monro's algorithm and Bernstein polynomials. We study the properties of the recursive estimator, as... 
stochastic approximation algorithm | Primary: 62E20 | Distribution estimation | 62L20 | Bernstein polynomial | DENSITY-FUNCTION | SMOOTH ESTIMATION | STATISTICS & PROBABILITY | REGULARLY VARYING SEQUENCES | PROBABILITY DENSITY | Recursive methods | Parameter estimation | Polynomials | Algorithms | Simulation | Computer simulation | Statistics | Statistics Theory
Journal Article
Diabetes Care, ISSN 0149-5992, 07/2016, Volume 39, Issue 7, pp. 1259 - 1266
Journal Article
Mathematical Methods of Statistics, ISSN 1066-5307, 10/2014, Volume 23, Issue 4, pp. 306 - 325
We apply the stochastic approximation method to construct a large class of recursive kernel estimators of a distribution function. We study the properties of... 
distribution estimation | Statistical Theory and Methods | primary 62E20, 62L20, 60G09 | stochastic approximation algorithm | Statistics | plug-in estimate | Analysis | Methods | Algorithms | Studies | Mathematical models | Statistical methods | Mathematical functions | Estimating techniques
Journal Article
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