Minimum variance

Abstract— This paper presents an adaptive control scheme for suppressing jitter in laser beams. The variable-order adaptive controller is based on an adaptive lattice filter that implicitly identifies the disturbance statistics from real-time sensor data. The multi-channel adaptive ...

... 27-51, 1970. 23. Jagannathan, R. and Ma, T., "Risk reduction in large portfolios: Why imposing the wrong constraints helps," J. Finance, v58, pp. 1651-1684, 2003. ... Lorenzo Garlappi: colleagues. Francisco J. Nogales: colleagues. Raman Uppal: colleagues. ...

Abstract A distributed estimation algorithm for sensor networks is proposed. A noisy time-varying signal is jointly tracked by a network of sensor nodes, in which each node computes its estimate as a weighted sum of its own and its neighbors' measurements and estimates. The weights are adaptively updated to minimize the variance of the estimation error. Both estimation and the parameter optimization is distributed; no central coordination of the nodes is required. An upper bound of the error variance in each node is derived. This ...

With institutional investors increasingly involved in alternative investments, portfolio optimisation within a large universe of hedge funds has become a key area for research. This paper develops a portfolio construction model that is specifically designed for funds of hedge funds, incorporating specific controls for operational limitations, data biases and incompleteness. Absolute performance is targeted by selecting funds according to their

Combining only SDSS LRG and WMAP data places robust constraints on many cosmological parameters that complement prior analyses of multiple data sets. The LRGs provide independent cross-checks on Om and the baryon fraction in good agreement with WMAP. Within the context of flat LCDM models, our LRG measurements complement WMAP by sharpening the constraints on the matter density, the neutrino density and the tensor amplitude by about a factor of two, giving Omega_m=0.24+-0.02 (1 sigma), sum m_nu < 0.9 eV (95%) and r<0.3 (95%). Baryon oscillations are clearly detected and provide a robust measurement of the comoving distance to the median survey redshift z=0.35 independent of curvature and dark energy properties. Within the LCDM framework, our power spectrum measurement improves the evidence for spatial flatness, sharpening the curvature constraint Omega_tot=1.05+-0.05 from WMAP alone to Omega_tot=1.003+-0.010. Assuming Omega_tot=1, the equation of state parameter is constrained to w=-0.94+-0.09, indicating the potential for more ambitious future LRG measurements to provide precision tests of the nature of dark energy. All these constraints are essentially independent of scales k>0.1h/Mpc and associated nonlinear complications, yet agree well with more aggressive published analyses where nonlinear modeling is crucial.

This paper presents a series of user parameter-free iterative Sparse Asymptotic Minimum Variance (SAMV) approaches for array processing applications based on the asymptotically minimum variance (AMV) criterion. With the assumption of abundant snapshots in the direction-of-arrival (DOA) estimation problem, the signal powers and noise variance are jointly estimated by the proposed iterative AMV approach, which is later proved to coincide with the Maximum Likelihood (ML) estimator. We then propose a series of power-based iterative SAMV approaches, which are robust against insufficient snapshots, coherent sources and arbitrary array geometries. Moreover, to overcome the direction grid limitation on the estimation accuracy, the SAMV-Stochastic ML (SAMV-SML) approaches are derived by explicitly minimizing a closed form stochastic ML cost function with respect to one scalar paramter, eliminating the need of any additional grid refinement techniques. To assist the performance evaluation, approximate solutions to the SAMV approaches are also provided for high signal-to-noise ratio (SNR) and low SNR scenarios. Finally, numerical examples are generated to compare the performances of the proposed approaches with those of the existing ones.

Dynamic hedging effectiveness for soybean farmers in Rondonópolis (MT) with futures contracts of BM&F is calculated through optimal hedge determination, using the bivariate GARCH BEKK model, which considers the conditional correlations of the prices series, comparing the results with the minimum variance model effectiveness, calculated by OLS, the unhedged and the naïve hedge positions. The financial effectiveness of the dynamic