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Artículos de revistas sobre el tema "Kernel estimates"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 13 de febrero de 2022

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1

Jones, M. C. "VARIABLE KERNEL DENSITY ESTIMATES AND VARIABLE KERNEL DENSITY ESTIMATES." Australian Journal of Statistics 32, no. 3 (1990): 361–71. http://dx.doi.org/10.1111/j.1467-842x.1990.tb01031.x.

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2

Qian, Zhongmin. "Gradient estimates and heat kernel estimates." Proceedings of the Royal Society of Edinburgh: Section A Mathematics 125, no. 5 (1995): 975–90. http://dx.doi.org/10.1017/s0308210500022599.

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In the first part of this paper, Yau's estimates for positive L-harmonic functions and Li and Yau's gradient estimates for the positive solutions of a general parabolic heat equation on a complete Riemannian manifold are obtained by the use of Bakry and Emery's theory. In the second part we establish a heat kernel bound for a second-order differential operator which has a bounded and measurable drift, using Girsanov's formula.

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3

Wang, Jiaping. "Global heat kernel estimates." Pacific Journal of Mathematics 178, no. 2 (1997): 377–98. http://dx.doi.org/10.2140/pjm.1997.178.377.

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4

Van Kerm, Philippe. "Adaptive Kernel Density Estimation." Stata Journal: Promoting communications on statistics and Stata 3, no. 2 (2003): 148–56. http://dx.doi.org/10.1177/1536867x0300300204.

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This insert describes the module akdensity. akdensity extends the official kdensity that estimates density functions by the kernel method. The extensions are of two types: akdensity allows the use of an “adaptive kernel” approach with varying, rather than fixed, bandwidths; and akdensity estimates pointwise variability bands around the estimated density functions.

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5

Staniswalis, Joan G., and Vanessa Cooper. "Kernel Estimates of Dose Response." Biometrics 44, no. 4 (1988): 1103. http://dx.doi.org/10.2307/2531739.

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6

Brown, Philip J., and Peter W. K. Rundell. "Kernel Estimates for Categorical Data." Technometrics 27, no. 3 (1985): 293–99. http://dx.doi.org/10.1080/00401706.1985.10488054.

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7

Ghosh, Anil K., Probal Chaudhuri, and Debasis Sengupta. "Classification Using Kernel Density Estimates." Technometrics 48, no. 1 (2006): 120–32. http://dx.doi.org/10.1198/004017005000000391.

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8

Franke, J., and W. Hardle. "On Bootstrapping Kernel Spectral Estimates." Annals of Statistics 20, no. 1 (1992): 121–45. http://dx.doi.org/10.1214/aos/1176348515.

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9

SAMIUDDIN, M., and G. M. EL-SAYYAD. "On nonparametric kernel density estimates." Biometrika 77, no. 4 (1990): 865–74. http://dx.doi.org/10.1093/biomet/77.4.865.

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10

Metafune, G., D. Pallara, and A. Rhandi. "Kernel estimates for Schrödinger operators." Journal of Evolution Equations 6, no. 3 (2006): 433–57. http://dx.doi.org/10.1007/s00028-006-0259-6.

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11

Małecki, Jacek, Grzegorz Serafin, and Tomasz Zorawik. "Fourier–Bessel heat kernel estimates." Journal of Mathematical Analysis and Applications 439, no. 1 (2016): 91–102. http://dx.doi.org/10.1016/j.jmaa.2016.02.051.

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12

ALBANESE, CLAUDIO. "KERNEL CONVERGENCE ESTIMATES FOR DIFFUSIONS WITH CONTINUOUS COEFFICIENTS." International Journal of Theoretical and Applied Finance 14, no. 07 (2011): 979–1004. http://dx.doi.org/10.1142/s0219024911006619.

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Bidirectional valuation models are based on numerical methods to obtain kernels of parabolic equations. Here we address the problem of robustness of kernel calculations vis a vis floating point errors from a theoretical standpoint. We are interested in kernels of one-dimensional diffusion equations with continuous coefficients as evaluated by means of explicit discretization schemes of uniform step h > 0 in the limit as h → 0. We consider both semidiscrete triangulations with continuous time and explicit Euler schemes with time step so small that the Courant condition is satisfied. We find

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13

Meyers, N. M., D. O. Huett, S. C. Morris, L. M. McFadyen, and C. A. McConchie. "Investigation of sampling procedures to determine macadamia fruit quality in orchards." Australian Journal of Experimental Agriculture 39, no. 8 (1999): 1007. http://dx.doi.org/10.1071/ea99072.

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Summary. Macadamia kernel quality estimates are of fundamental importance to understanding tree responses to many experimental treatments and orchard management protocols. Experimental measures of macadamia kernel quality, collected under field conditions, traditionally rely on the average of 100 fruit, sampled from the estimated peak in fruit drop. To detect changes in kernel quality over a single season, we measured variation in fruit quality of macadamia cv. 344. To sample this variation we measured 10 fruit from 6 blocks of 3 trees at each of 7 sites, over 4 harvests made at monthly interv

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14

Dungey, Nick. "On Gaussian kernel estimates on groups." Colloquium Mathematicum 100, no. 1 (2004): 77–90. http://dx.doi.org/10.4064/cm100-1-7.

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15

Bernicot, Frédéric, Thierry Coulhon, and Dorothee Frey. "Sobolev algebras through heat kernel estimates." Journal de l’École polytechnique — Mathématiques 3 (2016): 99–161. http://dx.doi.org/10.5802/jep.30.

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16

Priebe, Carey E., and David J. Marchette. "Alternating kernel and mixture density estimates." Computational Statistics & Data Analysis 35, no. 1 (2000): 43–65. http://dx.doi.org/10.1016/s0167-9473(00)00003-7.

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17

YANG, Meng. "Heat kernel estimates on Julia sets." Acta Mathematica Scientia 37, no. 5 (2017): 1399–414. http://dx.doi.org/10.1016/s0252-9602(17)30081-4.

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18

Staniswalis, Joan G. "Local Bandwidth Selection for Kernel Estimates." Journal of the American Statistical Association 84, no. 405 (1989): 284–88. http://dx.doi.org/10.1080/01621459.1989.10478767.

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19

Jones, M. C. "Discretized and Interpolated Kernel Density Estimates." Journal of the American Statistical Association 84, no. 407 (1989): 733–41. http://dx.doi.org/10.1080/01621459.1989.10478827.

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20

Murugan, Mathav. "Quasisymmetric uniformization and heat kernel estimates." Transactions of the American Mathematical Society 372, no. 6 (2019): 4177–209. http://dx.doi.org/10.1090/tran/7713.

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21

Metzger, Bernd, and Peter Stollmann. "Heat Kernel Estimates on Weighted Graphs." Bulletin of the London Mathematical Society 32, no. 4 (2000): 477–83. http://dx.doi.org/10.1112/s0024609300007153.

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22

Kunze, Markus, Luca Lorenzi, and Abdelaziz Rhandi. "Kernel estimates for nonautonomous Kolmogorov equations." Advances in Mathematics 287 (January 2016): 600–639. http://dx.doi.org/10.1016/j.aim.2015.09.029.

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23

Parente, Jacquelyn Dawn, J. Geoffrey Chase, Knut Möller, and Geoffrey M. Shaw. "Kernel density estimates for sepsis classification." Computer Methods and Programs in Biomedicine 188 (May 2020): 105295. http://dx.doi.org/10.1016/j.cmpb.2019.105295.

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24

Al-Qassem, Hussain, Leslie Cheng, and Yibiao Pan. "Endpoint Estimates for Oscillatory Singular Integrals with Hölder Class Kernels." Journal of Function Spaces 2019 (January 16, 2019): 1–7. http://dx.doi.org/10.1155/2019/8561402.

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We prove the uniform L1→L1,∞ and HE1→L1 boundedness of oscillatory singular integral operators whose kernels are the products of an oscillatory factor with bilinear phase and a Calderón-Zygmund kernel K(x,y) satisfying a Hölder condition. This Hölder condition appreciably weakens the C1 condition imposed in existing literature.

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25

Ding, Yong, and Shanzhen Lu. "Hardy spaces estimates for multilinear operators with homogeneous kernels." Nagoya Mathematical Journal 170 (2003): 117–33. http://dx.doi.org/10.1017/s0027763000008552.

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AbstractIn this paper the authors prove that a class of multilinear operators formed by the singular integral or fractional integral operators with homogeneous kernels are bounded operators from the product spaces Lp1 × Lp2 × · · · × LpK (ℝn) to the Hardy spaces Hq (ℝn) and the weak Hardy space Hq,∞(ℝn), where the kernel functions Ωij satisfy only the Ls-Dini conditions. As an application of this result, we obtain the (Lp, Lq) boundedness for a class of commutator of the fractional integral with homogeneous kernels and BMO function.

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26

Prazenica, Richard J., and Andrew J. Kurdila. "Volterra Kernel Identification Using Triangular Wavelets." Journal of Vibration and Control 10, no. 4 (2004): 597–622. http://dx.doi.org/10.1177/1077546304038269.

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The Nblterra series provides a convenient framework for the representation of nonlinear dynamical systems. One of the main drawbacks of this approach, however, is the large number of terns that are often needed to represent Wblterra kernels. In this paper we present an approach whereby wavelets are used to obtain low-order estimates of first-order and second-order blterra kernels. Several constructions of tensorproduct wavelets have been employed for some%blterra kernel approximations. In this paper, a triangular wavelet basis is constructed for the representation of the triangular fonn of the

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27

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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28

Li, Liangpan, and Alexander Strohmaier. "Heat kernel estimates for general boundary problems." Journal of Spectral Theory 6, no. 4 (2016): 903–19. http://dx.doi.org/10.4171/jst/147.

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29

Jorgensen, Palle, and Myung-Sin Song. "Reproducing Kernel Hilbert Space vs. Frame Estimates." Mathematics 3, no. 3 (2015): 615–25. http://dx.doi.org/10.3390/math3030615.

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30

Nowak, Adam, and Peter Sjögren. "Sharp estimates of the Jacobi heat kernel." Studia Mathematica 218, no. 3 (2013): 219–44. http://dx.doi.org/10.4064/sm218-3-2.

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31

Phillips, Jeff M., and Wai Ming Tai. "Near-Optimal Coresets of Kernel Density Estimates." Discrete & Computational Geometry 63, no. 4 (2019): 867–87. http://dx.doi.org/10.1007/s00454-019-00134-6.

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32

Chen, Zhen-Qing, Panki Kim, Takashi Kumagai, and Jian Wang. "Heat kernel estimates for time fractional equations." Forum Mathematicum 30, no. 5 (2018): 1163–92. http://dx.doi.org/10.1515/forum-2017-0192.

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AbstractIn this paper, we establish existence and uniqueness of weak solutions to general time fractional equations and give their probabilistic representations. We then derive sharp two-sided estimates for fundamental solutions of a family of time fractional equations in metric measure spaces.

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33

Nowak, Adam, Peter Sjögren, and Tomasz Z. Szarek. "Sharp estimates of the spherical heat kernel." Journal de Mathématiques Pures et Appliquées 129 (September 2019): 23–33. http://dx.doi.org/10.1016/j.matpur.2018.10.002.

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34

Müller, Hans-Georg, and Thomas Schmitt. "Kernel and Probit Estimates in Quantal Bioassay." Journal of the American Statistical Association 83, no. 403 (1988): 750–59. http://dx.doi.org/10.1080/01621459.1988.10478658.

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35

Cho, Hong Rae, and Soohyun Park. "BERGMAN KERNEL ESTIMATES FOR GENERALIZED FOCK SPACES." East Asian mathematical journal 33, no. 1 (2017): 37–44. http://dx.doi.org/10.7858/eamj.2017.004.

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36

Muller, Hans-Georg, and Thomas Schmitt. "Kernel and Probit Estimates in Quantal Bioassay." Journal of the American Statistical Association 83, no. 403 (1988): 750. http://dx.doi.org/10.2307/2289301.

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37

Mimica, Ante. "Heat kernel estimates for subordinate Brownian motions." Proceedings of the London Mathematical Society 113, no. 5 (2016): 627–48. http://dx.doi.org/10.1112/plms/pdw043.

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38

Seifert, Christian. "Kernel estimates for perturbations of positive semigroups." PAMM 14, no. 1 (2014): 1007–8. http://dx.doi.org/10.1002/pamm.201410483.

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39

Taylor, Charles C., Kanti V. Mardia, Marco Di Marzio, and Agnese Panzera. "Validating protein structure using kernel density estimates." Journal of Applied Statistics 39, no. 11 (2012): 2379–88. http://dx.doi.org/10.1080/02664763.2012.710898.

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40

Roussas, George G. "Kernel estimates under association: strong uniform consistency." Statistics & Probability Letters 12, no. 5 (1991): 393–403. http://dx.doi.org/10.1016/0167-7152(91)90028-p.

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41

Davies, Tilman M., and Adrian Baddeley. "Fast computation of spatially adaptive kernel estimates." Statistics and Computing 28, no. 4 (2017): 937–56. http://dx.doi.org/10.1007/s11222-017-9772-4.

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42

Kohler, Michael, Adam Krzyżak, and Harro Walk. "Strong consistency of automatic kernel regression estimates." Annals of the Institute of Statistical Mathematics 55, no. 2 (2003): 287–308. http://dx.doi.org/10.1007/bf02530500.

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43

Ozabaci, Deniz, and Daniel J. Henderson. "Additive kernel estimates of returns to schooling." Empirical Economics 48, no. 1 (2014): 227–51. http://dx.doi.org/10.1007/s00181-014-0877-8.

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44

El-Fallah, O., Y. Elmadani, and K. Kellay. "Kernel and capacity estimates in Dirichlet spaces." Journal of Functional Analysis 276, no. 3 (2019): 867–95. http://dx.doi.org/10.1016/j.jfa.2018.03.017.

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45

Nualart, Eulalia. "Exponential divergence estimates and heat kernel tail." Comptes Rendus Mathematique 338, no. 1 (2004): 77–80. http://dx.doi.org/10.1016/j.crma.2003.11.015.

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46

Baxter, M. J., C. C. Beardah, and R. V. S. Wright. "Some Archaeological Applications of Kernel Density Estimates." Journal of Archaeological Science 24, no. 4 (1997): 347–54. http://dx.doi.org/10.1006/jasc.1996.0119.

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47

Abdous, Belkacem, and Alain Berlinet. "Pointwise Improvement of Multivariate Kernel Density Estimates." Journal of Multivariate Analysis 65, no. 2 (1998): 109–28. http://dx.doi.org/10.1006/jmva.1998.1742.

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48

Michener, P. M., J. K. Pataky, and D. G. White. "Transmission of Erwinia stewartii from Plants to Kernels and Reactions of Corn Hybrids to Stewart's Wilt." Plant Disease 86, no. 2 (2002): 167–72. http://dx.doi.org/10.1094/pdis.2002.86.2.167.

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Stewart's wilt reactions of 98 food-grade, white corn hybrids, 3 yellow dent corn hybrids, and 23 sweet corn hybrids and infection of kernels by E. stewartii were evaluated in 1998, 1999, and 2000. Stewart's wilt symptoms were rated from 1 (no appreciable spread of symptoms) to 9 (dead plants) following inoculation. The mean Stewart's wilt ratings for the food-grade, white corn and yellow dent corn hybrids were 1.9, 2.4, and 2.9 in 1998, 1999, and 2000, respectively. The mean Stewart's wilt ratings for the sweet corn hybrids were 3.8, 4.2, and 4.6 in 1998, 1999, and 2000, respectively. Hybrids

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49

Varopoulos, N. Th. "Gaussian Estimates in Lipschitz Domains." Canadian Journal of Mathematics 55, no. 2 (2003): 401–31. http://dx.doi.org/10.4153/cjm-2003-018-9.

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50

Khaki, Saeed, Hieu Pham, Ye Han, Andy Kuhl, Wade Kent, and Lizhi Wang. "Convolutional Neural Networks for Image-Based Corn Kernel Detection and Counting." Sensors 20, no. 9 (2020): 2721. http://dx.doi.org/10.3390/s20092721.

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Precise in-season corn grain yield estimates enable farmers to make real-time accurate harvest and grain marketing decisions minimizing possible losses of profitability. A well developed corn ear can have up to 800 kernels, but manually counting the kernels on an ear of corn is labor-intensive, time consuming and prone to human error. From an algorithmic perspective, the detection of the kernels from a single corn ear image is challenging due to the large number of kernels at different angles and very small distance among the kernels. In this paper, we propose a kernel detection and counting m

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