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Maximum-Likelihood Estimation For Diffusion Processes Via Closed-Form Density Expansions
asymptotic expansion diffusion discrete observation maximum-likelihood estimation transition density
2016/1/25
This paper proposes a widely applicable method of approximate maximum-likelihood estimation for multivariate diffusion process from discretely sampled data. A closed-form asymptotic expansion for tran...
Maximum-Likelihood Estimation For Diffusion Processes Via Closed-Form Density Expansions
asymptotic expansion diffusion discrete observation maximum-likelihood estimation transition density
2016/1/20
This paper proposes a widely applicable method of approximate maximum-likelihood estimation for multivariate diffusion process from discretely sampled data. A closed-form asymptotic expansion for tran...
Limit theorems for kernel density estimators under dependent samples
Kernel density estimator consistency convergence rate mixing rate
2013/6/14
In this paper, we construct a moment inequality for mixing dependent random variables, it is of independent interest. As applications, the consistency of the kernel density estimation is investigated....
Efficient Density Estimation via Piecewise Polynomial Approximation
Efficient Density Estimation Piecewise Polynomial Approximation
2013/6/14
We give a highly efficient "semi-agnostic" algorithm for learning univariate probability distributions that are well approximated by piecewise polynomial density functions. Let $p$ be an arbitrary dis...
Probit transformation for kernel density estimation on the unit interval
transformation kernel density estimator boundary bias local likelihood density estimation local log-polynomial density estimation
2013/4/27
Kernel estimation of a probability density function supported on the unit interval has proved difficult, because of the well known boundary bias issues a conventional kernel density estimator would ne...
A comparative study of new cross-validated bandwidth selectors for kernel density estimation
kernel density estimation data-adaptive bandwidth selection indirect cross-validation do-validation.
2012/11/22
Recent contributions to kernel smoothing show that the performance of cross-validated bandwidth selectors improve significantly from indirectness. Indirect crossvalidation first estimates the classica...
Exploring wind direction and SO2 concentration by circular-linear density estimation
Circular distributions Circular kernel estimation Circular{linear data Copula.
2012/9/17
The study of environmental problems usually requires the description of variables with di er-ent nature and the assessment of relations between them. In this work, an algorithm for exible estimation o...
Aggregating density estimators: an empirical study
Machine Learning Histogram Kernel Density Estimator Bootstrap,Bagging Boosting, Stacking.
2012/9/19
We present some new density estimation algorithms obtainedby bootstrap ag-gregation like Bagging. Our algorithms are analyzed and empirically compared to other methods found in the statistical literat...
We propose a method for nonparametric density estimation that exhibits robustness to contamination of the training sample. This method achieves robustness by combining a traditional kernel density est...
The Shape of the Noncentral Chi-square Density
distribution theory log-concavity log-convexity noncentral distribution
2011/7/6
A noncentral chi-square density is log-concave if the degree of freedom is nu>=2. We complement this known result by showing that, for each 0 there exists lambda_nu>0 such that the chi-square wi...
k-Nearest neighbor density estimation on Riemannian Manifolds
Asymptotic results Density estimation Meteorological applications
2011/7/6
In this paper, we consider a k-nearest neighbor kernel type estimator when the random variables belong in a Riemannian manifolds.
Relative Density-Ratio Estimation for Robust Distribution Comparison
Density ratio Pearson divergence Outlier detection
2011/7/6
Divergence estimators based on direct approximation of density-ratios without going through separate approximation of numerator and denominator densities have been successfully applied to machine lear...
Density Estimation and Classification via Bayesian Nonparametric Learning of Affine Subspaces
Dimension reduction Classi er Variable selection Nonparametric Bayes
2011/6/20
It is now practically the norm for data to be very high dimensional in areas such as genetics, machine
vision, image analysis and many others. When analyzing such data, parametric models are often to...
On the Levy density function
density function fractional calculus Mellin convolution operator
2011/3/21
In this paper, we introduce the Levy density function as the limit of a generalized Mittag-Leffler density function. The fractional integral equation for the generalized Mittag-Leffler density functio...
Concentration Inequalities and Confidence Bands for Needlet Density Estimators on Compact Homogeneous Manifolds
Concentration Inequalities Confidence Bands Density Estimators
2011/3/21
Let $X_1,...,X_n$ be a random sample from some unknown probability density $f$ defined on a compact homogeneous manifold $\mathbf M$ of dimension $d \ge 1$. Consider a 'needlet frame' $\{\phi_{j \eta}...