Dasgupta, Sutanoy and Pati, Debdeep and Jermyn, Ian H. and Srivastava, Anuj (2018) 'Shape-constrained and unconstrained density estimation using geometric exploration.', in 2018 IEEE Statistical Signal Processing Workshop (SSP 2018) : 10-13 June 2018, Freiburg im Breisgau, Germany. Piscataway: IEEE, pp. 358-362.
The problem of nonparametrically estimating probability density functions (pdfs) from observed data requires posing and solving optimization problems on the space of pdfs. We take a geometric approach and explore this space for optimization using actions of a time-warping group. One action, termed area preserving, is transitive and is applicable to the case of unconstrained density estimation. In this case, we take a two-step approach that involves obtaining any initial estimate of the pdf and then transforming it via this warping action to reach the final estimate by maximizing the log-likelihood function. Another action, termed mode-preserving, is useful in situations where the pdf is constrained in shape, i.e. the number of its modes is known. As earlier, we initialize the estimation with an arbitrary element of the correct shape class, and then search over all time warpings to reach the optimal pdf within that shape class. Optimization over warping functions is performed numerically using the geometry of the group of warping functions. These methods are illustrated using a number of simulated examples.
|Item Type:||Book chapter|
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1109/ssp.2018.8450768|
|Publisher statement:||© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Date accepted:||27 March 2018|
|Date deposited:||06 August 2018|
|Date of first online publication:||30 August 2018|
|Date first made open access:||30 August 2018|
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