We propose a mixed-effects vector auto-regressive (ME-VAR) model for studying brain effective connectivity. One common approach to investigating inter-regional associations in brain activity is the multivariate auto-regressive (VAR) model. The standard VAR model unrealistically assumes the connectivity structure to be identical across all participants in a study and therefore, could yield misle...

In Section 3 we showed that our method can have more power to detect o-eSNPs. Here we discuss its power to detect SNPs whose functional mechanisms have non-regulatory components. For simplicity we again consider only continuous Yi, Gi, and a single SNP Si in the ordinary linear model, where the variables have all been centered. We now consider the outcome model Yi = G T i αG + αSSi + i1, where ...

this research aims to use var as a risk measure to find the optimum portfolio in tehran stock exchange. in this research var which is calculated with parametric method by using the 15 daily returns of 100 companies from march 21, 2001 to november 22, 2007 was added to the markowitz model of portfolio optimization as additional constraint. by changing the accepted var and accepted confidence lev...

This paper proposes a Vector Autoregressive (VAR) model as a new technique for missing feature reconstruction in ASR. We model the spectral features using multiple VAR models. A VAR model predicts missing features as a linear function of a block of feature frames. We also propose two schemes for VAR training and testing. The experiments on AURORA-2 database have validated the modeling methodolo...

The Vector Autoregressive (VAR) model, the Error Correction Model (ECM), and the Kalman Filter Model (KFM) are used to forecast UK stock prices. The forecasting performance of the three models is compared using out of sample forecasting. The results show that the forecasting performance of the ECM is better than that of the VAR and the KFM, and that the VAR performs a forecasting better than th...

We consider the relative advantages of two advanced data assimilation systems, 4-D-Var and ensemble Kalman filter (EnKF), currently in use or under consideration for operational implementation. With the Lorenz model, we explore the impact of tuning assimilation parameters such as the assimilation window length and background error covariance in 4-D-Var, variance inflation in EnKF, and the effec...

Despite well-known shortcomings as a risk measure, Value-at-Risk (VaR) is still the industry and regulatory standard for the calculation of risk capital in banking and insurance. This paper is concerned with the numerical estimation of the VaR for a portfolio position as a function of different dependence scenarios on the factors of the portfolio. Besides summarizing the most relevant analytica...

simply stated, a vaR model is a model of the distribution of future profits and losses of a bank’s trading portfolio. vaR models combine information on a bank’s trading positions across various products with statistical estimations of the probability distribution of the underlying market factors and their relation to each other. the final output of a vaR model is a vaR estimate, which is define...

In this study, a vector autoregression (VAR) model with time-varying parameters (TVP) to predict the daily Indian rupee (INR)/US dollar (USD) exchange rates for the Indian economy is developed. The method is based on characterization of the TVP as an optimal control problem. The methodology is a blend of the flexible least squares and Kalman filter techniques. The out-of-sample forecasting perf...

Vector auto-regressive (VAR) models typically form the basis for constructing directed graphical models for investigating connectivity in a brain network with brain regions of interest (ROIs) as nodes. There are limitations in the standard VAR models. The number of parameters in the VAR model increases quadratically with the number of ROIs and linearly with the order of the model and thus due t...