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Estimation of Spatial Panel Data Models with Time Varying Spatial Weights Matrices
Spatial autoregression Panel data Time varying spatial weights matrices Fixed e¤ects Maximum likelihood Impact analysis
2016/1/20
This paper investigates the quasi-maximum likelihood (QML) estimation of spatial panel data models where spatial weights matrices can be time varying. We show that QML estimate is consistent and asymp...
From Feshbach-resonance managed Bose-Einstein condensates to anisotropic universes: Applications of the Ermakov-Pinney equation with time-dependent nonlinearity
Two-dimensional condensate time-varying magnetic confinement scattering wave function ordinary differential equations
2014/12/25
In this work we revisit the topic of two-dimensional Bose–Einstein condensates under the influence of time-dependent magnetic confinement and time-dependent scattering length. A moment approach reduce...
Topics in Multivariate Time Series Analysis: Statistical Control, Dimension Reduction Visualization and Thir Business Applications
Topics in Multivariate Time Series Analysis Statistical Control Dimension Reduction Visualization Their Business Applications
2014/10/28
Most business processes are, by nature, multivariate and autocorrelated. Highdimensionality is rooted in processes where more than one variable is considered simultaneously to provide a more comprehen...
Modelling time and vintage variability in retail credit portfolios: the decomposition approach
Age-period-cohort default Exogeneous EMV model Forecasting Macroeco-nomic Statistical model Vintage
2013/6/14
In this paper, we consider the problem of modelling historical data on retail credit portfolio performance, with a view to forecasting future performance, and facilitating strategic decision making. W...
Robust Hydraulic Fracture Monitoring (HFM) of Multiple Time Overlapping Events Using a Generalized Discrete Radon Transform
Robust Hydraulic Fracture Monitoring (HFM) Multiple Time Overlapping Events aGeneralized Discrete Radon Transform
2013/6/14
In this work we propose a novel algorithm for multiple-event localization for Hydraulic Fracture Monitoring (HFM) through the exploitation of the sparsity of the observed seismic signal when represent...
Fourier analysis of stationary time series in function space
Cumulants discrete Fourier transform functional data analy-sis functional time series periodogram operator spectral density operator weak depen-dence
2013/6/14
We develop the basic building blocks of a frequency domain framework for drawing statistical inferences on the second-order structure of a stationary sequence of functional data. The key element in su...
Inference and testing for structural change in time series of counts model
time series of counts Poisson autoregression likelihood estimation change-point semi-parametric test
2013/6/14
We consider here together the inference questions and the change-point problem in Poisson autoregressions (see Tj{\o}stheim, 2012). The conditional mean (or intensity) of the process is involved as a ...
Approximate Inference for Observation Driven Time Series Models with Intractable Likelihoods
Observation Driven Time Series Models Approximate Bayesian Computation Asymptotic Con-sistency Markov Chain Monte Carlo
2013/4/28
In the following article we consider approximate Bayesian parameter inference for observation driven time series models. Such statistical models appear in a wide variety of applications, including eco...
A Robust Bayesian Dynamic Linear Model to Detect Abrupt Changes in an Economic Time Series: The Case of Puerto Rico
Dynamic Models Consumer Price Index Bayesian Robustness
2013/4/28
Economic indicators time series are usually complex with high frequency data. The traditional time series methodology requires at least a preliminary transformation of the data to get stationarity. On...
The Identification of Thresholds and Time Delay in Self-Exciting Threshold a Model by Wavelet
threshold autoregressive model threshold time delay wavelet
2013/5/2
In this paper we studied about the wavelet identification of the thresholds and time delay for more general case without the constraint that the time delay is smaller than the order of the model. Here...
Asymptotic Normality of Estimates in Flexible Seasonal Time Series Model with Weak Dependent Error Terms
seasonal time series model local linear estimates consistency and asymptotic
2013/5/2
In this paper we considered a general seasonal time series model with K-dependent and \rambda-dependent errors, which are new concepts of dependence. In this model we derived consistency and asymptoti...
Statistical inference for discrete-time samples from affine stochastic delay differential equations
asymptotic normality composite likelihood consistency discrete time observation of continuous-time models prediction-based estimating functions pseudo-likelihood stochastic delay differential equation
2013/4/28
Statistical inference for discrete time observations of an affine stochastic delay differential equation is considered. The main focus is on maximum pseudo-likelihood estimators, which are easy to cal...
On a class of space-time intrinsic random functions
Fox’sH-function generalized covariance function Mat′ern covariance function
2013/4/28
Power law generalized covariance functions provide a simple model for describing the local behavior of an isotropic random field. This work seeks to extend this class of covariance functions to spatia...
Combining Dynamic Predictions from Joint Models for Longitudinal and Time-to-Event Data using Bayesian Model Averaging
Prognostic Modeling Risk Prediction
2013/4/27
The joint modeling of longitudinal and time-to-event data is an active area of statistics research that has received a lot of attention in the recent years. More recently, a new and attractive applica...
Environmental Time Series Interpolation Based on Spartan Random Processes
inference precision matrix gappy data atmospheric aerosol fine particulate PM2.5
2013/4/27
In many environmental applications, time series are either incomplete or irregularly spaced. We investigate the application of the Spartan random process to missing data prediction. We employ a novel ...