Kernel density estimators

March 5, 2016 — August 18, 2016

A nonparametric method of approximating something from data by assuming that it’s close to the data distribution convolved with some kernel.

This is especially popular the target is a probability density function; Then you are working with a kernel density estimator.

To learn about:

0.1 Bandwidth/kernel selection in density estimation

Bernacchia and Pigolotti (2011) has a neat hack: “self consistency” for simultaneous kernel and distribution inference, i.e. simultaneous deconvolution and bandwidth selection. The idea is removing bias by using simple spectral methods, thereby estimating a kernel which in a certain sense would generate the data that you just observed. The results look similar to finite-sample corrections for Gaussian scale parameter estimates, but are not quite Gaussian.

Question: could it work with mixture models too?

0.2 Mixture models

Where the number of kernels does not grow as fast as the number of data points, this becomes a mixture model; Or, if you’d like, kernel density estimates are a limiting case of mixture model estimates.

They are so clearly similar that I think it best we not make them both feel awkward by dithering about where the free parameters are. Anyway, they are filed separately. (Battey and Liu 2013; van de Geer 1996; Zeevi and Meir 1997)discuss some useful things common to various convex combination estimators.

0.3 Does this work with uncertain point locations?

The fact we can write the kernel density estimate as an integral with a convolution of Dirac deltas immediately suggests that we could write it as a convolution of something else, such as Gaussians. Can we recover well-behaved estimates in that case? This would be a kind of hierarchical model, possibly a typical Bayesian one.

0.4 Does this work with asymmetric kernels?

Almost all the kernel estimates I’ve seen require KDEs to be symmetric, because of Cline’s argument that asymmetric kernels are inadmissible in the class of all (possibly multivariate) densities. Presumably this implies \(\mathcal{C}_1\) distributions, i.e. once-differentiable ones. In particular admissible kernels are those which have “nonnegative Fourier transforms bounded by 1”, which implies symmetry about the axis. If we have an a priori constrained class of densities, this might not apply.

0.5 Fast Gauss Transform and Fast multipole methods

How to make these methods computationally feasible at scale. See Fast Gauss Transform and other related fast multipole methods.

1 References

Aalen. 1978. Nonparametric Inference for a Family of Counting Processes.” The Annals of Statistics.
Adelfio, and Schoenberg. 2009. Point Process Diagnostics Based on Weighted Second-Order Statistics and Their Asymptotic Properties.” Annals of the Institute of Statistical Mathematics.
Baddeley, Adrian, and Turner. 2006. Modelling Spatial Point Patterns in R.” In Case Studies in Spatial Point Process Modeling. Lecture Notes in Statistics 185.
Baddeley, A., Turner, Møller, et al. 2005. Residual Analysis for Spatial Point Processes (with Discussion).” Journal of the Royal Statistical Society: Series B (Statistical Methodology).
Barnes, and Hut. 1986. A Hierarchical O(N Log N) Force-Calculation Algorithm.” Nature.
Bashtannyk, and Hyndman. 2001. Bandwidth Selection for Kernel Conditional Density Estimation.” Computational Statistics & Data Analysis.
Battey, and Liu. 2013. Smooth Projected Density Estimation.” arXiv:1308.3968 [Stat].
Berman, and Diggle. 1989. Estimating Weighted Integrals of the Second-Order Intensity of a Spatial Point Process.” Journal of the Royal Statistical Society. Series B (Methodological).
Bernacchia, and Pigolotti. 2011. Self-Consistent Method for Density Estimation.” Journal of the Royal Statistical Society: Series B (Statistical Methodology).
Botev, Grotowski, and Kroese. 2010. Kernel Density Estimation via Diffusion.” The Annals of Statistics.
Cline. 1988. Admissible Kernel Estimators of a Multivariate Density.” The Annals of Statistics.
Crisan, and Míguez. 2014. Particle-Kernel Estimation of the Filter Density in State-Space Models.” Bernoulli.
Cucala. 2008. Intensity Estimation for Spatial Point Processes Observed with Noise.” Scandinavian Journal of Statistics.
Díaz-Avalos, Juan, and Mateu. 2012. Similarity Measures of Conditional Intensity Functions to Test Separability in Multidimensional Point Processes.” Stochastic Environmental Research and Risk Assessment.
Diggle, Peter J. 1979. On Parameter Estimation and Goodness-of-Fit Testing for Spatial Point Patterns.” Biometrics.
Diggle, Peter. 1985. A Kernel Method for Smoothing Point Process Data.” Journal of the Royal Statistical Society. Series C (Applied Statistics).
Doosti, and Hall. 2015. Making a Non-Parametric Density Estimator More Attractive, and More Accurate, by Data Perturbation.” Journal of the Royal Statistical Society: Series B (Statistical Methodology).
Ellis. 1991. Density Estimation for Point Processes.” Stochastic Processes and Their Applications.
Geenens. 2014. Probit Transformation for Kernel Density Estimation on the Unit Interval.” Journal of the American Statistical Association.
Greengard, and Strain. 1991. The Fast Gauss Transform.” SIAM Journal on Scientific and Statistical Computing.
Hall. 1987. On Kullback-Leibler Loss and Density Estimation.” The Annals of Statistics.
Hall, and Park. 2002. New Methods for Bias Correction at Endpoints and Boundaries.” The Annals of Statistics.
Helmers, Wayan Mangku, and Zitikis. 2003. Consistent Estimation of the Intensity Function of a Cyclic Poisson Process.” Journal of Multivariate Analysis.
Ho, and Walker. 2020. Multivariate Smoothing via the Fourier Integral Theorem and Fourier Kernel.” arXiv:2012.14482 [Math, Stat].
Ibragimov. 2001. Estimation of Analytic Functions.” In Institute of Mathematical Statistics Lecture Notes - Monograph Series.
Jones, and Henderson. 2009. Maximum Likelihood Kernel Density Estimation: On the Potential of Convolution Sieves.” Computational Statistics & Data Analysis.
Koenker, and Mizera. 2006. Density Estimation by Total Variation Regularization.” Advances in Statistical Modeling and Inference.
Liu, Chang, and Ma. 2012. Blind Image Deblurring by Spectral Properties of Convolution Operators.” arXiv:1209.2082 [Cs].
Malec, and Schienle. 2014. Nonparametric Kernel Density Estimation Near the Boundary.” Computational Statistics & Data Analysis.
Marshall, and Hazelton. 2010. Boundary Kernels for Adaptive Density Estimators on Regions with Irregular Boundaries.” Journal of Multivariate Analysis.
O’Brien, Kashinath, Cavanaugh, et al. 2016. A Fast and Objective Multidimensional Kernel Density Estimation Method: fastKDE.” Computational Statistics & Data Analysis.
Panaretos, and Konis. 2012. Nonparametric Construction of Multivariate Kernels.” Journal of the American Statistical Association.
Rathbun. 1996. Estimation of Poisson Intensity Using Partially Observed Concomitant Variables.” Biometrics.
Raykar, and Duraiswami. 2005. The Improved Fast Gauss Transform with Applications to Machine Learning.”
Silverman. 1982. On the Estimation of a Probability Density Function by the Maximum Penalized Likelihood Method.” The Annals of Statistics.
Smith, and Lewicki. 2005. Efficient Coding of Time-Relative Structure Using Spikes.” Neural Computation.
Stein. 2005. Space-Time Covariance Functions.” Journal of the American Statistical Association.
van de Geer. 1996. Rates of Convergence for the Maximum Likelihood Estimator in Mixture Models.” Journal of Nonparametric Statistics.
van Lieshout. 2011. On Estimation of the Intensity Function of a Point Process.” Methodology and Computing in Applied Probability.
Yang, Duraiswami, and Davis. 2004. Efficient Kernel Machines Using the Improved Fast Gauss Transform.” In Advances in Neural Information Processing Systems.
Yang, Duraiswami, Gumerov, et al. 2003. Improved Fast Gauss Transform and Efficient Kernel Density Estimation.” In Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2. ICCV ’03.
Zeevi, and Meir. 1997. Density Estimation Through Convex Combinations of Densities: Approximation and Estimation Bounds.” Neural Networks: The Official Journal of the International Neural Network Society.
Zhang, and Karunamuni. 2010. Boundary Performance of the Beta Kernel Estimators.” Journal of Nonparametric Statistics.