The robust or sandwich estimator is common to estimate the covariance matrix of the estimated regression parameter for generalized estimating equation (GEE) method to analyze longitudinal data. However, the robust estimator would underestimate the variance under a small sample size. We propose an alternative covariance estimator to the robust estimator to improve the small-sample bias in the GE...

We study the relative risk of value and growth stocks. We find that time-varying risk goes in the right direction in explaining the value premium. Value betas tend to covary positively, and growth betas tend to covary negatively with the expected market risk premium. Our inference differs from that of previous studies because we sort betas on the expected market risk premium, instead of on the ...

In distributed and decentralized state estimation systems, fusion methods are employed to systematically combine multiple estimates of the state into a single, more accurate estimate. An often encountered problem in the fusion process relates to unknown common information that is shared by the estimates to be fused and is responsible for correlations. If the correlation structure is unknown to ...

Robust covariance estimates are required in many applications. Here, a promising adaptive robust scale estimator is extended to this problem and compared to other robust estimators. Often the performance analysis of covariance estimators is performed from the perspective of the final application. However, different applications have different requirements, hence we make a comparison based on so...

SITE model was developed to study the response of tropical ecosystems according to environmental conditions for estimating of energy fluxes. This study analyzed the net ecosystem of CO2 exchange (NEE) by ecosystem modeling and field measurements in a semi deciduous forest located at 50 km of Sinop municipal district, Mato Grosso, Brazil. Continuous measurements of canopy CO2 storage and fluxes ...

This paper is concerned with filtering nonlinear multivariate time series. A new approximate Bayesian algorithm is proposed which generates sample points and corresponding probability weights that match exactly the predicted values of average marginal skewness and average marginal kurtosis of the unobserved state variables, in addition to matching their mean and the covariance matrix. The perfo...

In this paper, we propose a low complexity two-dimensional direction of arrival (2D-DOA) estimation in monostatic multiple-input multiple-output (MIMO) radar with double parallel uniform linear arrays. The proposed algorithm uses propagator method (PM), which does not require any eigen-value decomposition of the covariance matrix and singular value decomposition of the received data, obtains cl...

Standard maximum likelihood estimation cannot be applied to discrete energy-based models in the general case because the computation of exact model probabilities is intractable. Recent research has seen the proposal of several new estimators designed specifically to overcome this intractability, but virtually nothing is known about their theoretical properties. In this paper, we present a gener...

Mean-preserving and covariance preservingmatchings are introduced that can be obtained with conditional, randomized matching on sub-populations of a large control group. Under moment conditions it is shown that these matchings are, respectively, equal percent bias reducing (EPBR) and variance proportionatemodifying (PM) for linear functions of the covariates and their standardizations. The resu...

In this paper, the problem of Direction of Arrival (DOA) estimation of multiple sources is addressed considering possible coherence loss along the impinging wavefronts. The loss results from wave propagation through a fluctuating medium and leads to a decreasing signal correlation from sensor to sensor. Two new algorithms are proposed that are significantly less computationally complex than the...