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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
2016/1/26
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...
Estimation of fixed effects panel regression models with separable and nonseparable space-time filters
Panel data Spatial cointegration Explosive roots Fixed e¤ects
2016/1/25
This paper considers a quasi-maximum likelihood estimation for a linear panel data model with time and individual …xed e¤ects, where the disturbances have dynamic and spatial correlations which might ...
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...
Estimation of fixed effects panel regression models with separable and nonseparable space-time filters
Spatial autoregression Panel data Spatial cointegration Explosive roots Fixed e¤ects
2016/1/20
This paper considers a quasi-maximum likelihood estimation for a linear panel data model with time and individual …xed e¤ects, where the disturbances have dynamic and spatial correlations which might ...
QML estimation of spatial dynamic panel data models with time varying spatial weights matrices
Spatial autoregression Dynamic panels Time varying spatial weights matrix Fixed ef- fects Maximum likelihood
2016/1/19
This paper investigates the quasi-maximum likelihood estimation of spatial dynamic panel data mod-els where spatial weights matrices can be time varying. We …nd that QML estimate is consistent and asy...
IMPROVED SEMI-PARAMETRIC TIME SERIES MODELS OF AIR POLLUTION AND MORTALITY
Semiparametric regression time series Particulate Matter (PM) Generalized Additive Model Generalized Linear Model Mean Squared Error Bandwidth Selection
2015/8/21
In 2002, methodological issues around time series analyses of air pollution and health attracted the attention of the scientific community, policy makers, the press, and the diverse stakeholders conce...
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...
Dynamic Covariance Models for Multivariate Financial Time Series
Dynamic Covariance Models Multivariate Financial Time Series
2013/6/14
The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problem...
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...
Detecting Overlapping Temporal Community Structure in Time-Evolving Networks
Detecting Overlapping Temporal Community Structure Time-Evolving Networks
2013/5/2
We present a principled approach for detecting overlapping temporal community structure in dynamic networks. Our method is based on the following framework: find the overlapping temporal community str...