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In this paper we derive the asymptotic properties of GMM estimators for the spatial dynamic panel data model with fixed effects when n is large, and T can be large, but small relative to n. The GMM es...
In this paper we derive the asymptotic properties of GMM estimators for the spatial dynamic panel data model with fixed effects when n is large, and T can be large, but small relative to n. The GMM es...
Yu, de Jong and Lee (2008) establish asymptotic properties of quasi-maximum likelihood estimators for a stable spatial dynamic panel model with …xed e¤ects when both the number of individuals n and th...
In this paper we derive the asymptotic properties of GMM estimators for the spatial dynamic panel data model with …xed e¤ects when n is large, and T can be large, but small relative to n. The GMM esti...
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...
We consider the use of quadratic approximate value functions for stochastic control problems with input-affine dynamics and convex stage cost and constraints. Evaluating the approximate dynamic progra...
Combining dynamic renormalization with equation-free computational tools, we study the apparently asymptotically self-similar evolution of void distribution dynamics in the diffusion-deposition proble...
Time series of graphs are increasingly prevalent in modern data and pose unique challenges to visual exploration and pattern extraction. This paper describes the development and application of matrix ...
This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters. The alg...
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...
Models for complex systems are often built with more parameters than can be uniquely identified by available data. Because of the variety of causes, identifying a lack of parameter identifiability typ...
The focus of this work is on developing probabilistic models for user activity in social networks by incorporating the social network influence as perceived by the user. For this, we propose a coupled...
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...
With the increasing availability of public cloud computing facilities and scientific super computers, there is a great potential for making R available through public or shared resources. This allows ...
This article proposes a spatial dynamic structural equation model for the analysis of housing prices at the State level in the USA. The study contributes to the existing literature by extending the us...

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