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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...
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...
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...
Given a network of processes where each node has an initial scalar value, we consider the problem of computing their average asymptotically using a distributed, linear iterative algorithm. At each ite...
Recent analysis of Multiple-Input Multiple-Output (MIMO) communication systems, for example those involving antenna arrays, has shown that channel capacity increases linearly with the number of antenn...
This paper presents a new distributed power control algorithm for ad-hoc wire- less networks in random channel environments. Previous work in this area has focused on distributed power control for ad-...
This paper presents new distributed power and admission control algorithms for ad-hoc wireless networks in random channel environments. Previous work in this area has focused on distributed control fo...
We consider parameter estimation, hypothesis testing and vari-able selection for partially time-varying coefficient models. Our asymp-totic theory has the useful feature that it can allow dependent, n...
This paper examines the adaptive market hypothesis of Lo (2004, 2005) using the Ito and Noda’s (2012) non-Bayesian time-varying AR model in Japan. As shown in Ito and Noda (2012), their degree of mar...
Signal estimation from incomplete observations improves as more signal structure can be exploited in the inference process. Classic algorithms (e.g., Kalman filtering) have exploited strong dynamic st...
This paper provides an insight to the time-varying dynamics of the shape of the distribution of financial return series by proposing an exponential weighted moving average model that joint...
This paper focuses on an extension of the Limit Order Book (LOB) model with general shape introduced by Alfonsi, Fruth and Schied. Here, the additional feature allows a time-varying LOB depth. We solv...
The problem of test of fit for Vector AutoRegressive (VAR) processes with unconditionally heteroscedastic errors is studied. The volatility structure is deterministic but time-varying and allows for...
A trend in compressed sensing (CS) is to exploit struc- ture for improved reconstruction performance. In the basic CS model (i.e. the single measurement vec- tor model), exploiting the clustering s...
Bollerslev’s (1990) constant conditional correlation (CCC) and Engle’s (2002) dynamic conditional correlation (DCC) bivariate generalized autoregressive conditional heteroskedasticity (BGARCH) models ...

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