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On Smoothing Estimation For Seasonal Times Series With Long Cycles
Kernel estimator M-dependent seasonal-dummy ap- proach
2016/1/20
We consider a kernel smoothing estimator to the periodic component of seasonal time series which have quite large periodicity relative to the length of the time series. The estimator is formulated by ...
Non-linear dimensionality reduction: Riemannian metric estimation and the problem of geometric discovery
Non-linear dimensionality reduction Riemannian metric estimation the problem geometric discovery
2013/6/14
In recent years, manifold learning has become increasingly popular as a tool for performing non-linear dimensionality reduction. This has led to the development of numerous algorithms of varying degre...
Estimation of an Origin/Destination matrix: Application to a ferry transport data
constraint maximum likelihood estimation eigenvectors counts estimation
2013/6/14
The estimation of the number of passengers with the identical journey is a common problem for public transport authorities. This problem is also known as the Origin- Destination estimation (OD) proble...
Two-stage Benchmarking as Applied to Small Area Estimation
Two-stage Benchmarking as Applied to Small Area Estimation
2013/6/14
There has been recent growth in small area estimation due to the need for more precise estimation of small geographic areas, which has led to groups such as the U.S. Census Bureau, Google, and the RAN...
Adaptive estimation in nonparametric regression with one-sided errors
adaptive convergence rates non-regular regression frontier estimation bandwidth selection Lepski's method minimax optimality Pickands estimator
2013/6/14
We consider the model of non-regular nonparametric regression where smoothness constraints are imposed on the regression function and the regression errors are assumed to decay with some sharpness lev...
A general approach of least squares estimation and optimal filtering
Least squares Optimal filtering Matched filter Noise Optimization Power Spectrum Density
2013/6/17
The least squares method allows fitting parameters of a mathematical model from experimental data. This article proposes a general approach of this method. After introducing the method and giving a fo...
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation
Local Privacy Minimax Bounds Sharp Rates Probability Estimation
2013/6/14
We provide a detailed study of the estimation of probability distributions---discrete and continuous---in a stringent setting in which data is kept private even from the statistician. We give sharp mi...
Risk Measure Estimation On Fiegarch Processes
Long Memory Models Volatility Risk Measure Estimation FIEGARCH Processes
2013/6/17
We consider the Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroskedasticity process, denoted by FIEGARCH(p,d,q), introduced by Bollerslev and Mikkelsen (1996). We pre...
Penalized importance sampling for parameter estimation in stochastic differential equations
Chronic wasting disease Euler-Maruyama scheme Maximum likelihood estimation Partially observed discrete sparse data Penalized importance sampling Stochastic di
2013/6/14
We consider the problem of estimating parameters of stochastic differential equations with discrete-time observations that are either completely or partially observed. The transition density between t...
Estimation in Systems of Ordinary Differential Equations Linear in the Parameters
local polynomials Lotka-Volterra nonparamet-ric regression ordinary differential equation plug-in estimators
2013/6/14
Many phenomena in biology, chemistry, physics, and engineering are modeled by a system of possibly nonlinear ordinary differential equations that are linear in their unknown constants. Current methods...
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...
Stable Estimation of a Covariance Matrix Guided by Nuclear Norm Penalties
Covariance estimation Regularization Condition number Canonical correlation analysis Discriminant analysis Clustering
2013/6/14
Estimation of covariance matrices or their inverses plays a central role in many statistical methods. For these methods to work reliably, estimated matrices must not only be invertible but also well-c...
We prove an almost sure weak limit theorem for simple linear rank statistics for samples with continuous distributions functions. As a corollary the result extends to samples with ties, and the vector...
Functional and Parametric Estimation in a Semi- and Nonparametric Model with Application to Mass-Spectrometry Data
Local linear regression Bandwidth selection Nonparamet-ric estimation
2013/6/13
Motivated by modeling and analysis of mass-spectrometry data, a semi- and nonparametric model is proposed that consists of a linear parametric component for individual location and scale and a nonpara...
Moment based estimation of supOU processes and a related stochastic volatility model
generalized method of moments Ornstein-Uhlenbeck type process L
2013/6/14
After a quick review of superpositions of OU (supOU) processes, integrated supOU processes and the supOU SV model we estimate these processes by using the generalized method of moments. We show that t...