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Journal articles on the topic 'Kernel parameter estimation'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Liu, Qing, David Pitt, Xibin Zhang, and Xueyuan Wu. "A Bayesian Approach to Parameter Estimation for Kernel Density Estimation via Transformations." Annals of Actuarial Science 5, no. 2 (2011): 181–93. http://dx.doi.org/10.1017/s1748499511000030.

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AbstractIn this paper, we present a Markov chain Monte Carlo (MCMC) simulation algorithm for estimating parameters in the kernel density estimation of bivariate insurance claim data via transformations. Our data set consists of two types of auto insurance claim costs and exhibits a high-level of skewness in the marginal empirical distributions. Therefore, the kernel density estimator based on original data does not perform well. However, the density of the original data can be estimated through estimating the density of the transformed data using kernels. It is well known that the performance

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Elie, Romuald. "Double Kernel Estimation of Sensitivities." Journal of Applied Probability 46, no. 3 (2009): 791–811. http://dx.doi.org/10.1239/jap/1253279852.

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In this paper we address the general issue of estimating the sensitivity of the expectation of a random variable with respect to a parameter characterizing its evolution. In finance, for example, the sensitivities of the price of a contingent claim are called the Greeks. A new way of estimating the Greeks has recently been introduced in Elie, Fermanian and Touzi (2007) through a randomization of the parameter of interest combined with nonparametric estimation techniques. In this paper we study another type of estimator that turns out to be closely related to the score function, which is well k

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Elie, Romuald. "Double Kernel Estimation of Sensitivities." Journal of Applied Probability 46, no. 03 (2009): 791–811. http://dx.doi.org/10.1017/s002190020000588x.

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In this paper we address the general issue of estimating the sensitivity of the expectation of a random variable with respect to a parameter characterizing its evolution. In finance, for example, the sensitivities of the price of a contingent claim are called the Greeks. A new way of estimating the Greeks has recently been introduced in Elie, Fermanian and Touzi (2007) through a randomization of the parameter of interest combined with nonparametric estimation techniques. In this paper we study another type of estimator that turns out to be closely related to the score function, which is well k

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Hovda, Sigve. "Properties of Transmetric Density Estimation." International Journal of Statistics and Probability 5, no. 3 (2016): 63. http://dx.doi.org/10.5539/ijsp.v5n3p63.

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Transmetric density estimation is a generalization of kernel density estimation that is proposed in Hovda(2014) and Hovda (2016), This framework involves the possibility of making assumptions on the kernel of the distribution to improve convergence orders and to reduce the number of dimensions in the graphical display. In this paper we show that several state-of-the-art nonparametric, semiparametric and even parametric methods are special cases of this formulation, meaning that there is a unified approach. Moreover, it is shown that parameters can be trained using unbiased cross-validation. Wh

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Baszczyńska, Aleksandra Katarzyna. "One Value of Smoothing Parameter vs Interval of Smoothing Parameter Values in Kernel Density Estimation." Acta Universitatis Lodziensis. Folia Oeconomica 6, no. 332 (2018): 73–86. http://dx.doi.org/10.18778/0208-6018.332.05.

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Ad hoc methods in the choice of smoothing parameter in kernel density estimation, although often used in practice due to their simplicity and hence the calculated efficiency, are characterized by quite big error. The value of the smoothing parameter chosen by Silverman method is close to optimal value only when the density function in population is the normal one. Therefore, this method is mainly used at the initial stage of determining a kernel estimator and can be used only as a starting point for further exploration of the smoothing parameter value. This paper presents ad hoc methods for

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Barbeito, Inés, and Ricardo Cao. "Computationally Efficient Bootstrap Expressions for Bandwidth Selection in Nonparametric Curve Estimation." Proceedings 2, no. 18 (2018): 1164. http://dx.doi.org/10.3390/proceedings2181164.

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Bootstrap methods are used for bandwidth selection in: (1) nonparametric kernel density estimation with dependent data (smoothed stationary bootstrap and smoothed moving blocks bootstrap), and (2) nonparametric kernel hazard rate estimation (smoothed bootstrap). In these contexts, four new bandwidth parameter selectors are proposed based on closed bootstrap expressions of the MISE of the kernel density estimator (case 1) and two approximations of the kernel hazard rate estimation (case 2). These expressions turn out to be very useful since Monte Carlo approximation is no longer needed. Finally

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Hwang, TaeHyun, Rhee Man Kil, Hyun Ku Lee, et al. "Estimation of Jamming Parameters based on Gaussian Kernel Function Networks." Journal of the Korea Institute of Military Science and Technology 23, no. 1 (2020): 1–10. https://doi.org/10.9766/kimst.2020.23.1.001.

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Effective jamming in electronic warfare depends on proper jamming technique selection and jamming parameter estimation. For this purpose, this paper proposes a new method of estimating jamming parameters using Gaussian kernel function networks. In the proposed approach, a new method of determining the optimal structure and parameters of Gaussian kernel function networks is proposed. As a result, the proposed approach estimates the jamming parameters in a reliable manner and outperforms other methods such as the DNN(Deep Neural Network) and SVM(Support Vector Machine) estimation models.

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Schmid, H., M. P. Nash, A. A. Young, O. Röhrle, and P. J. Hunter. "A Computationally Efficient Optimization Kernel for Material Parameter Estimation Procedures." Journal of Biomechanical Engineering 129, no. 2 (2006): 279–83. http://dx.doi.org/10.1115/1.2540860.

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Estimating material parameters is an important part in the study of soft tissue mechanics. Computational time can easily run to days, especially when all available experimental data are taken into account. The material parameter estimation procedure is examplified on a set of homogeneous simple shear experiments to estimate the orthotropic constitutive parameters of myocardium. The modification consists of changing the traditional least-squares approach to a weighted least-squares. This objective function resembles a L2-norm type integral which is approximated using Gaussian quadrature. This r

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Chandra, Novita Eka, Sri Haryatmi, and Zulaela Zulaela. "REGRESI NONPARAMETRIK KERNEL ADJUSTED." Jurnal Ilmiah Matematika dan Pendidikan Matematika 7, no. 1 (2015): 1. http://dx.doi.org/10.20884/1.jmp.2015.7.1.2894.

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Nadaraya Watson's kernel adjusted regression estimator is an estimator whose kernel is taken from the family of scale-location associated with the classical kernel density estimator. Based on these estimator, it can be obtained optimal bandwith and scale parameter. This estimator gives a better estimation results compared with Naradaya Watson's classical kernel regression estimator. This is proven by the small grade MSE which is given by this estimator.

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Lakhdar, Yissam, and El Hassan Sbai. "Online Variable Kernel Estimator." International Journal of Operations Research and Information Systems 8, no. 1 (2017): 58–92. http://dx.doi.org/10.4018/ijoris.2017010104.

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In this work, the authors propose a novel method called online variable kernel estimation of the probability density function (pdf). This new online estimator combines the characteristics and properties of two estimators namely nearest neighbors estimator and the Parzen-Rosenblatt estimator. Their approach allows a compact online adaptation of the estimated probability density function from the new arrival data. The performance of the online variable kernel estimator (OVKE) depends on the choice of the bandwidth. The authors present in this article a new technique for determining the optimal s

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Kulczycki, Piotr. "An Algorithm for Bayes Parameter Identification." Journal of Dynamic Systems, Measurement, and Control 123, no. 4 (1999): 611–14. http://dx.doi.org/10.1115/1.1409552.

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This paper deals with the task of parameter identification using the Bayes estimation method, which makes it possible to take into account the differing consequences of positive and negative estimation errors. The calculation procedures are based on the kernel estimators technique. The final result constitutes a complete algorithm usable for obtaining the value of the Bayes estimator on the basis of an experimentally obtained random sample. An elaborated method is provided for numerical computations.

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Christou, Christina, and Nikitas Pittis. "KERNEL AND BANDWIDTH SELECTION, PREWHITENING, AND THE PERFORMANCE OF THE FULLY MODIFIED LEAST SQUARES ESTIMATION METHOD." Econometric Theory 18, no. 4 (2002): 948–61. http://dx.doi.org/10.1017/s0266466602184076.

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This paper examines several practical issues regarding the implementation of the Phillips and Hansen fully modified least squares (FMLS) method for the estimation of a cointegrating vector. Various versions of this method arise by selecting between standard and prewhitened kernel estimation and between parametric and nonparametric automatic bandwidth estimators and also among alternative kernels. A Monte Carlo study is conducted to investigate the finite-sample properties of the alternative versions of the FMLS procedure. The results suggest that the prewhitened kernel estimator of Andrews and

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Mahzabeen, Sabiha, and Mezbahur Rahman. "ADAPTIVE SMOOTHING PARAMETER IN KERNEL DENSITY ESTIMATION." Far East Journal of Mathematical Sciences (FJMS) 118, no. 2 (2019): 107–27. http://dx.doi.org/10.17654/ms118020107.

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El-hayet, Ladaouri Nour, and Mouloud Cherfaoui. "Bayesian approach in the nonparametric conditional density estimation." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e11284. https://doi.org/10.54021/seesv5n2-592.

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In the literature, it is well known that in kernel density estimation, plug-in and cross-validation techniques, for the selection of the smoothing parameter, tend to provide under-or over-smoothed estimators when the sample size is small or medium, or the function to be estimated is complex. To overcome this latest problem, recently, the Bayesian approach has been proposed as an alternative to these classical methods. In this paper, we restricted attention to extending the idea of the Bayes rule to estimate the smoothing parameter of the conditional density kernel estimation. We are interested

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Hall, Peter, and Joel L. Horowitz. "Bandwidth Selection in Semiparametric Estimation of Censored Linear Regression Models." Econometric Theory 6, no. 2 (1990): 123–50. http://dx.doi.org/10.1017/s0266466600005089.

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Quantile and semiparametric M estimation are methods for estimating a censored linear regression model without assuming that the distribution of the random component of the model belongs to a known parametric family. Both methods require estimating derivatives of the unknown cumulative distribution function of the random component. The derivatives can be estimated consistently using kernel estimators in the case of quantile estimation and finite difference quotients in the case of semiparametric M estimation. However, the resulting estimates of derivatives, as well as parameter estimates and i

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Siloko, Israel Uzuazor, Wilson Nwankwo, and Edith Akpevwe Siloko. "A new family of kernels from the beta polynomial kernels with applications in density estimation." International Journal of Advances in Intelligent Informatics 6, no. 3 (2020): 235. http://dx.doi.org/10.26555/ijain.v6i3.456.

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One of the fundamental data analytics tools in statistical estimation is the non-parametric kernel method that involves probability estimates production. The method uses the observations to obtain useful statistical information to aid the practicing statistician in decision making and further statistical investigations. The kernel techniques primarily examine essential characteristics in a data set, and this research aims to introduce new kernel functions that can easily detect inherent properties in any given observations. However, accurate application of kernel estimator as data analytics ap

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Cortés López, Juan Carlos, and Marc Jornet Sanz. "Improving Kernel Methods for Density Estimation in Random Differential Equations Problems." Mathematical and Computational Applications 25, no. 2 (2020): 33. http://dx.doi.org/10.3390/mca25020033.

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Kernel density estimation is a non-parametric method to estimate the probability density function of a random quantity from a finite data sample. The estimator consists of a kernel function and a smoothing parameter called the bandwidth. Despite its undeniable usefulness, the convergence rate may be slow with the number of realizations and the discontinuity and peaked points of the target density may not be correctly captured. In this work, we analyze the applicability of a parametric method based on Monte Carlo simulation for the density estimation of certain random variable transformations.

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Han, Yuecai, Zhe Yin, and Dingwen Zhang. "Parameter Estimation of Linear Stochastic Differential Equations with Sparse Observations." Symmetry 14, no. 12 (2022): 2500. http://dx.doi.org/10.3390/sym14122500.

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We consider parameter estimation for linear stochastic differential equations with independent experiments observed at infrequent and irregularly spaced follow-up times. The maximum likelihood method is used to obtain an asymptotically consistent estimator. A kernel-weighted score function is proposed for the parameter in drift terms. The strong consistency and the rate of convergence of the estimator are obtained. The numerical results show that the proposed estimator performs well with moderate sample sizes.

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Sabatier, Jocelyn, and Christophe Farges. "Time-Domain Fractional Behaviour Modelling with Rational Non-Singular Kernels." Axioms 13, no. 2 (2024): 99. http://dx.doi.org/10.3390/axioms13020099.

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This paper proposes a solution to model fractional behaviours with a convolution model involving non-singular kernels and without using fractional calculus. The non-singular kernels considered are rational functions of time. The interest of this class of kernel is demonstrated with a pure power law function that can be approximated in the time domain by a rational function whose pole and zeros are interlaced and linked by geometric laws. The Laplace transform and frequency response of this class of kernel is given and compared with an approximation found in the literature. The comparison revea

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Jesús, A. Fajardo. "Boundary Estimation with the Fuzzy Set Regression Estimator." Divulgaciones Matemáticas 23-24, no. 1-2 (2024): 82–106. https://doi.org/10.5281/zenodo.11540455.

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In order to extend the properties of the fuzzy set regression estimation method and provide new results related to the nonparametric regression estimation problems not based on kernels, this paper analyzes the possible boundary effects, if any, of the fuzzy set regression estimator and presents a criterion to remove it. Moreover, a boundary fuzzy set estimator is proposedwhich is defined as a particular class of fuzzy set regression estimators, where the bias, variance, mean squared error and function that minimizes the mean squared error of the proposed estimator are

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Elie, Romuald, Jean-David Fermanian, and Nizar Touzi. "Kernel estimation of Greek weights by parameter randomization." Annals of Applied Probability 17, no. 4 (2007): 1399–423. http://dx.doi.org/10.1214/105051607000000186.

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Hart, Timothy, and Paul Zandbergen. "Kernel density estimation and hotspot mapping." Policing: An International Journal of Police Strategies & Management 37, no. 2 (2014): 305–23. http://dx.doi.org/10.1108/pijpsm-04-2013-0039.

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Purpose – The purpose of this paper is to examine the effects of user-defined parameters settings (e.g. interpolation method, grid cell size, and bandwidth) on the predictive accuracy of crime hotspot maps produced from kernel density estimation (KDE). Design/methodology/approach – The influence of variations in parameter settings on prospective KDE maps is examined across two types of interpersonal violence (e.g. aggravated assault and robbery) and two types of property crime (e.g. commercial burglary and motor vehicle theft). Findings – Results show that interpolation method has a considerab

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Bassem Khalaf, Narjes, and Lekaa Ali Mohammed. "Comparison of Some Methods for Estimating Nonparametric Binary Logistic Regression." Journal of Economics and Administrative Sciences 29, no. 135 (2023): 56–67. http://dx.doi.org/10.33095/jeas.v29i135.2505.

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In this research, the methods of Kernel estimator (nonparametric density estimator) were relied upon in estimating the two-response logistic regression, where the comparison was used between the method of Nadaraya-Watson and the method of Local Scoring algorithm, and optimal Smoothing parameter λ was estimated by the methods of Cross-validation and generalized Cross-validation, bandwidth optimal λ has a clear effect in the estimation process. It also has a key role in smoothing the curve as it approaches the real curve, and the goal of using the Kernel estimator is to modify the observations s

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Fadillah, Nur, Priliany Audina Dariah, Anisa Anggraeni, Nur Cahyani, and Lilies Handayani. "Comparison of Gaussian and Epancehnikov Kernels." Tadulako Social Science and Humaniora Journal 3, no. 1 (2022): 13–22. http://dx.doi.org/10.22487/sochum.v3i1.15745.

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Kernel regression is a nonparametric analysis with smoothing method. Smoothing has become synonymous with nonparametric methods used to estimate functions. The purpose of smoothing is to remove variability from data that has no effect so that the characteristics of the data will appear clear. Kernel regression has a flexible form and the mathematical calculations are easy to adjust. In kernel regression, an estimator is known which is usually used to estimate the regression function, namely the Nadaraya-Watson estimator. This study aims to show how to estimate data using nonparametric regressi

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Lamusu, Febriolah, Tedy Machmud, and Resmawan Resmawan. "Estimator Nadaraya-Watson dengan Pendekatan Cross Validation dan Generalized Cross Validation untuk Mengestimasi Produksi Jagung." Indonesian Journal of Applied Statistics 3, no. 2 (2021): 85. http://dx.doi.org/10.13057/ijas.v3i2.42125.

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<p>Nadaraya-Watson Estimator with kernel approach depends on two-parameter, those are kernel function and bandwidth choice. However, between the two of them, bandwidth choice gave a huge impact on the result of the estimation. By minimizing the value of Mean Square Error (MSE), Cross-Validation (CV) and Generalized Cross-Validation (GCV) gave the optimal bandwidth value. In this research, corn production was considered as the dependent variable, while the planted area, harvested area, and the fertilizer as the independent variable. The result of this research showed that Nadaraya-Watson

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Privault, Nicolas, and Xiao Wei. "Integration by Parts for Point Processes and Monte Carlo Estimation." Journal of Applied Probability 44, no. 3 (2007): 806–23. http://dx.doi.org/10.1239/jap/1189717546.

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We develop an integration by parts technique for point processes, with application to the computation of sensitivities via Monte Carlo simulations in stochastic models with jumps. The method is applied to density estimation with respect to the Lebesgue measure via a modified kernel estimator which is less sensitive to variations of the bandwidth parameter than standard kernel estimators. This applies to random variables whose densities are not analytically known and requires the knowledge of the point process jump times.

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Privault, Nicolas, and Xiao Wei. "Integration by Parts for Point Processes and Monte Carlo Estimation." Journal of Applied Probability 44, no. 03 (2007): 806–23. http://dx.doi.org/10.1017/s0021900200003442.

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We develop an integration by parts technique for point processes, with application to the computation of sensitivities via Monte Carlo simulations in stochastic models with jumps. The method is applied to density estimation with respect to the Lebesgue measure via a modified kernel estimator which is less sensitive to variations of the bandwidth parameter than standard kernel estimators. This applies to random variables whose densities are not analytically known and requires the knowledge of the point process jump times.

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Wang, Hailun, and Daxing Xu. "Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function." Journal of Control Science and Engineering 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/3614790.

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Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We

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Ogbeide, Efosa Michael, and Joseph Erunmwosa Osemwenkhae. "On the Use of a Modified Intersection of Confidence Intervals (MICIH) Kernel Density Estimation Approach." ATHENS JOURNAL OF SCIENCES 8, no. 4 (2021): 309–32. http://dx.doi.org/10.30958/ajs.8-4-4.

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Density estimation is an important aspect of statistics. Statistical inference often requires the knowledge of observed data density. A common method of density estimation is the kernel density estimation (KDE). It is a nonparametric estimation approach which requires a kernel function and a window size (smoothing parameter H). It aids density estimation and pattern recognition. So, this work focuses on the use of a modified intersection of confidence intervals (MICIH) approach in estimating density. The Nigerian crime rate data reported to the Police as reported by the National Bureau of Stat

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Moon, Kevin, Kumar Sricharan, Kristjan Greenewald, and Alfred Hero. "Ensemble Estimation of Information Divergence †." Entropy 20, no. 8 (2018): 560. http://dx.doi.org/10.3390/e20080560.

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Recent work has focused on the problem of nonparametric estimation of information divergence functionals between two continuous random variables. Many existing approaches require either restrictive assumptions about the density support set or difficult calculations at the support set boundary which must be known a priori. The mean squared error (MSE) convergence rate of a leave-one-out kernel density plug-in divergence functional estimator for general bounded density support sets is derived where knowledge of the support boundary, and therefore, the boundary correction is not required. The the

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Ye, Fei, Xin Wang, Dong Hui Peng, and Chuan Hai Jiao. "The Method of Optimal Group Based on the Kernel Density Estimation and the Close Degree." Advanced Materials Research 989-994 (July 2014): 3689–92. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3689.

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The optimal group is an important problem of histogram algorithm, and how to confirm group number has not a quantitative rule. So the concept of the close degree is imported to make the close degree between the upper contour line of histogram and the PDF(probability density function) of parameter as the judging criteria of optimal group. With the unknown of the PDF of parameter, the improved kernel density estimation algorithm can pre-select and estimate the PDF. This improved kernel density estimation algorithm combine the selection of fixed window and variable window's width to achieve the w

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Orava, Jan. "K-nearest neighbour kernel density estimation, the choice of optimal k." Tatra Mountains Mathematical Publications 50, no. 1 (2011): 39–50. http://dx.doi.org/10.2478/v10127-011-0035-z.

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ABSTRACT The k-nearest neighbour kernel density estimationmethod is a special type of the kernel density estimation method with the local choice of the bandwidth. An advantage of this estimator is that smoothing varies according to the number of observations in a particular region. The crucial problem is how to estimate the value of the parameter k. In the paper we discuss the problem of choosing the parameter k in a way that minimizes the value of the asymptotic mean integrated square error (AMISE). We define the class of the modified cosine densities that meet the requirements given by the A

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Xie, Daiyu, Yuan Fu, Sheng Yang, Youhui Yang, and Mingyuan Chen. "Wind Power Interval Prediction Based on Robust Kernel Density Estimation." Journal of Physics: Conference Series 2534, no. 1 (2023): 012011. http://dx.doi.org/10.1088/1742-6596/2534/1/012011.

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Abstract Wind power output has a high degree of randomness, so it is difficult to describe it accurately with some typical probability distribution. The extreme values influence the sample’s general non-parametric kernel density estimation method, and the estimated results are relatively conservative. A wind power interval prediction method based on robust kernel density estimation is proposed to improve the compactness and accuracy of interval prediction. In probability estimation, this method will assign a small weight to the extreme sample data to reduce its influence on the probability den

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Maity, Arnab, and Debapriya Sengupta. "A Perturbation Technique for Sample Moment Matching in Kernel Density Estimation." Calcutta Statistical Association Bulletin 56, no. 1-4 (2005): 161–88. http://dx.doi.org/10.1177/0008068320050510.

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Summary The fundamental idea of kernel smoothing technique can be recognized as one-parameter data perturbation with a smooth density. The usual kernel density estimates might not match arbitrary sample moments calculated from the unsmoothed data. A technique based on two-parameter data perturbation is developed for sample moment matching in kernel density estimation. It is shown that the moments calculated from the resulting tuned kernel density estimate can be made arbitrarily close to the raw sample moments. Moreover, the pointwise rate of MISE of the resulting density estimates remains opt

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Agbokou, Komi, and Yaogan Mensah. "Varying bandwidth parameter method on Kernel Gini index estimation." Annals of the University of Craiova Mathematics and Computer Science Series 50, no. 1 (2023): 29–41. http://dx.doi.org/10.52846/ami.v50i1.1602.

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Most of measures of income inequality are derived from the Lorenz curve and many authors state that the Gini index is the best single measure of inequality. The present paper reviews some of theoretical properties of the Lorenz curve and provides a non-parametric estimate of the Gini index and the almost sure convergence of this estimate. And to confirm the performance of the estimator, a simulation on real data was carried out.

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Pfahlberg, A., O. Gefeller, and R. Weißbach. "Double-smoothing in Kernel Hazard Rate Estimation." Methods of Information in Medicine 47, no. 02 (2008): 167–73. http://dx.doi.org/10.3414/me0447.

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Summary Objectives: In oncological studies, the hazard rate can be used to differentiate subgroups of the study population according to their patterns of survival risk over time. Nonparametric curve estimation has been suggested as an exploratory means of revealing such patterns. The decision about the type of smoothing parameter is critical for performance in practice. In this paper, we study data-adaptive smoothing. Methods: A decade ago, the nearest-neighbor bandwidth was introduced for censored data in survival analysis. It is specified by one parameter, namely the number of nearest neighb

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Ledl, Thomas. "Kernel Density Estimation: Theory and Application in Discriminant Analysis." Austrian Journal of Statistics 33, no. 3 (2016): 267–79. http://dx.doi.org/10.17713/ajs.v33i3.441.

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Nowadays, one can find a huge set of methods to estimate the density function of a random variable nonparametrically. Since the first version of the most elementary nonparametric density estimator (the histogram) researchers produced a vast amount of ideas especially corresponding to the issue of choosing the bandwidth parameter in a kernel density estimator model. To focus not only on a descriptive application, the model seems to be quite suitable for application in discriminant analysis, where (multivariate) class densities are the basis for the assignment of a vector to a given class. Thisa

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Wang, Zengke, Yi Li, and Wei Xu. "A Blind Parameter Estimation Method of Frequency Hopping Signal with Low SNR." International Journal of Circuits, Systems and Signal Processing 15 (April 5, 2021): 248–53. http://dx.doi.org/10.46300/9106.2021.15.28.

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In order to effectively estimate the parameters of the frequency hopping signals under low signal-to-noise ratio (SNR), a blind parameter estimation method based on the modified discrete time Wigner-Ville distribution (MDTWVD) is proposed. We choose a low order Chebyshev polynomial as the kernel function for reducing the cross-term. Then, the parameters of the frequency hopping signals are finally obtained from the MDTWVD. The simulation experiment results show that the method used in this paper can effectively and accurately estimate frequency hopping signals parameters, especially under low

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MILIVOJEVIC, Zoran, Bojan PRLINCEVIC, and Natasa SAVIC. "Optimization Parameter of the 1P Keys Interpolation Kernel Implemented in the Correlation Algorithm for Estimating the Fundamental Frequency of the Speech Signal." Eurasia Proceedings of Science Technology Engineering and Mathematics 16 (December 31, 2021): 153–61. http://dx.doi.org/10.55549/epstem.1068581.

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The first part of this paper describes an algorithm for estimating the fundamental frequency F0 of a speech signal, using an autocorrelation algorithm. After that, it was shown that, due to the discrete structure of the autocorrelation function, the accuracy of the fundamental frequency estimate largely depends on the sampling period TS. Then, in order to increase the accuracy of the estimation, an interpolation of the correlation function is performed. Interpolation is performed using a one parameter (1P) Keys interpolation kernel. The second part of the paper presents an experiment in which

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Turan, Jan, Zoran Bojkovic, Peter Filo, Andreja Samcovic, and L'ubos Ovsenik. "Signal processing with continuous Kernel Hough transform." Facta universitatis - series: Electronics and Energetics 18, no. 1 (2005): 113–26. http://dx.doi.org/10.2298/fuee0501113t.

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The paper deals with new modification of Hough transform - Continuous Kernel Hough transform. Definition of Continuous Kernel Hough transform, image processing, system identification and basics of parameter estimation are presented.

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Liu, Shanshan, Qingbin Huang, and Minghui Wang. "Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation." Applied Sciences 12, no. 20 (2022): 10606. http://dx.doi.org/10.3390/app122010606.

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Multi-frame super-resolution makes up for the deficiency of sensor hardware and significantly improves image resolution by using the information of inter-frame and intra-frame images. Inaccurate blur kernel estimation will enlarge the distortion of the estimated high-resolution image. Therefore, multi-frame blind super resolution with unknown blur kernel is more challenging. For the purpose of reducing the impact of inaccurate motion estimation and blur kernel estimation on the super-resolved image, we propose a novel method combining motion estimation, blur kernel estimation and super resolut

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Troudi, Molka, and Faouzi Ghorbel. "The Generalised Plug-in Algorithm for the Diffeomorphism Kernel Estimate." International Journal of Mathematics and Computers in Simulation 15 (November 27, 2021): 128–33. http://dx.doi.org/10.46300/9102.2021.15.24.

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The optimal value of the smoothing parameter of the Kernel estimator can be obtained by the well known Plug-in algorithm. The optimality is realised in the sense of Mean Integrated Square Error (MISE). In this paper, we propose to generalise this algorithm to the case of the difficult problem of the estimation of a distribution which has a bounded support. The proposed algorithm consists in searching the optimal smoothing parameter by iterations from the expression of MISE of the kernel-diffeomorphism estimator. By some simulations applied to some distribution having a support bounded and semi

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Nelson, J. D. B., R. I. Damper, S. R. Gunn, and B. Guo. "Signal theory for SVM kernel design with applications to parameter estimation and sequence kernels." Neurocomputing 72, no. 1-3 (2008): 15–22. http://dx.doi.org/10.1016/j.neucom.2008.01.034.

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Ataee, Mohammad Sadegh, Yasser Maghsoudi, Hooman Latifi, and Farhad Fadaie. "Improving Estimation Accuracy of Growing Stock by Multi-Frequency SAR and Multi-Spectral Data over Iran’s Heterogeneously-Structured Broadleaf Hyrcanian Forests." Forests 10, no. 8 (2019): 641. http://dx.doi.org/10.3390/f10080641.

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Via providing various ecosystem services, the old-growth Hyrcanian forests play a crucial role in the environment and anthropogenic aspects of Iran and beyond. The amount of growing stock volume (GSV) is a forest biophysical parameter with great importance in issues like economy, environmental protection, and adaptation to climate change. Thus, accurate and unbiased estimation of GSV is also crucial to be pursued across the Hyrcanian. Our goal was to investigate the potential of ALOS-2 and Sentinel-1’s polarimetric features in combination with Sentinel-2 multi-spectral features for the GSV est

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Wang, Mingquan, Xiaohua Ma, Xinrui Wang, Jun Wang, Xiuqing Zhou, and Qibing Gao. "Smoothing Estimation of Parameters in Censored Quantile Linear Regression Model." Mathematics 13, no. 2 (2025): 192. https://doi.org/10.3390/math13020192.

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In this paper, we propose a smoothing estimation method for censored quantile regression models. The method associates the convolutional smoothing estimation with the loss function, which is quadratically derivable and globally convex by using a non-negative kernel function. Thus, the parameters of the regression model can be computed by using the gradient-based iterative algorithm. We demonstrate the convergence speed and asymptotic properties of the smoothing estimation for large samples in high dimensions. Numerical simulations show that the smoothing estimation method for censored quantile

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ZHANG, LI, WEIDA ZHOU, and LICHENG JIAO. "SUPPORT VECTOR MACHINES BASED ON THE ORTHOGONAL PROJECTION KERNEL OF FATHER WAVELET." International Journal of Computational Intelligence and Applications 05, no. 03 (2005): 283–303. http://dx.doi.org/10.1142/s1469026805001489.

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Recently the study on the theory of wavelets shows that the wavelets have not only the multi-resolution property both in frequency and time domain, but also the good approximation ability. SVMs based on the statistical learning theory are a kind of general and effective learning machines, and have described for us the nice application blueprint in machine learning domain. There exists a bottleneck problem, or the pre-selection of kernel parameter for SVMs. In this paper, the orthogonal projection kernels of father wavelet (OPFW kernels) are introduced into SVMs. In doing so SVMs based on the O

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Stroud, Jonathan R., Matthias Katzfuss, and Christopher K. Wikle. "A Bayesian Adaptive Ensemble Kalman Filter for Sequential State and Parameter Estimation." Monthly Weather Review 146, no. 1 (2018): 373–86. http://dx.doi.org/10.1175/mwr-d-16-0427.1.

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AbstractThis paper proposes new methodology for sequential state and parameter estimation within the ensemble Kalman filter. The method is fully Bayesian and propagates the joint posterior distribution of states and parameters over time. To implement the method, the authors consider three representations of the marginal posterior distribution of the parameters: a grid-based approach, a Gaussian approximation, and a sequential importance sampling (SIR) approach with kernel resampling. In contrast to existing online parameter estimation algorithms, the new method explicitly accounts for paramete

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Chen, Xiyuan, Di Liu, Yu Zhang, Xiao Liu, Yuan Xu, and Chunfeng Shi. "Robust motion blur kernel parameter estimation for star image deblurring." Optik 230 (March 2021): 166288. http://dx.doi.org/10.1016/j.ijleo.2021.166288.

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Li, Peng, Francesca Boem, Gilberto Pin, and Thomas Parisini. "Kernel-Based Simultaneous Parameter-State Estimation for Continuous-Time Systems." IEEE Transactions on Automatic Control 65, no. 7 (2020): 3053–59. http://dx.doi.org/10.1109/tac.2019.2953146.

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TAYLOR, CHARLES C. "Bootstrap choice of the smoothing parameter in kernel density estimation." Biometrika 76, no. 4 (1989): 705–12. http://dx.doi.org/10.1093/biomet/76.4.705.

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