Search results for "ESTIMATOR"
showing 10 items of 313 documents
Channel Estimation and Interference Cancellation for MIMO-OFDM Systems
2007
This paper proposes a new channel estimation method and a new interference cancellation scheme for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in the presence of intersymbol interference (IS1). The proposed channel estimation method uses special training sequences (TSs) to have a desirable crest-factor of the transmitted training signal, and to prevent the influence of ISI on the channel estimation performance. By using the recommended training sequences, the ill-conditioned problem of the least square (LS) filter integrated in the proposed channel estimator can be avoided. The proposed interference cancellation scheme uses the estimated cha…
Analysis of Spatially and Temporally Overlapping Events with Application to Image Sequences
2006
Counting spatially and temporally overlapping events in image sequences and estimating their shape-size and duration features are important issues in some applications. We propose a stochastic model, a particular case of the nonisotropic 3D Boolean model, for performing this analysis: the temporal Boolean model. Some probabilistic properties are derived and a methodology for parameter estimation from time-lapse image sequences is proposed using an explicit treatment of the temporal dimension. We estimate the mean number of germs per unit area and time, the mean grain size and the duration distribution. A wide simulation study in order to assess the proposed estimators showed promising resul…
Canonical versus microcanonical analysis of first-order phase transitions
1998
Abstract I discuss the relation between canonical and microcanonical analyses of first-order phase transitions. In particular it is shown that the microcanonical Maxwell construction is equivalent to the equal-peak-height criterion often employed in canonical simulations. As a consequence the microcanonical finite-size estimators for the transition point, latent heat and interface tension are identical to standard estimators in the canonical ensemble. Special emphasis is placed on various ways for estimating interface tensions. The theoretical considerations are illustrated with numerical data for the two-dimensional 10-state Potts model.
An extension of the censored gaussian lasso estimator
2019
The conditional glasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. In this paper we propose an extension to censored data.
Anomaly detection in dynamic systems using weak estimators
2011
Accepted version of an article from the journal: ACM transactions on internet technology. Published version available from the ACM: http://dx.doi.org/10.1145/1993083.1993086 Anomaly detection involves identifying observations that deviate from the normal behavior of a system. One of the ways to achieve this is by identifying the phenomena that characterize “normal” observations. Subsequently, based on the characteristics of data learned from the “normal” observations, new observations are classified as being either “normal” or not. Most state-of-the-art approaches, especially those which belong to the family of parameterized statistical schemes, work under the assumption that the underlying…
An Extension of the DgLARS Method to High-Dimensional Relative Risk Regression Models
2020
In recent years, clinical studies, where patients are routinely screened for many genomic features, are becoming more common. The general aim of such studies is to find genomic signatures useful for treatment decisions and the development of new treatments. However, genomic data are typically noisy and high dimensional, not rarely outstripping the number of patients included in the study. For this reason, sparse estimators are usually used in the study of high-dimensional survival data. In this paper, we propose an extension of the differential geometric least angle regression method to high-dimensional relative risk regression models.
Monotonicity of Bayes estimators
2013
Let X = (X1; : : : ;Xn) be a sample from a distribution with density (x;θ), θ∈Θ⊂R. In this article the Bayesian estimation of the parameter is considered.We examine whether the Bayes estimators of are pointwise ordered when the prior distributions are partially ordered. Various cases of loss function are studied. A lower bound for the survival function of the normal distribution is obtained.
On the Efficiency of Affine Invariant Multivariate Rank Tests
1998
AbstractIn this paper the asymptotic Pitman efficiencies of the affine invariant multivariate analogues of the rank tests based on the generalized median of Oja are considered. Formulae for asymptotic relative efficiencies are found and, under multivariate normal and multivariatetdistributions, relative efficiencies with respect to Hotelling'sT2test are calculated.
On the Online Classification of Data Streams Using Weak Estimators
2016
In this paper, we propose a novel online classifier for complex data streams which are generated from non-stationary stochastic properties. Instead of using a single training model and counters to keep important data statistics, the introduced online classifier scheme provides a real-time self-adjusting learning model. The learning model utilizes the multiplication-based update algorithm of the Stochastic Learning Weak Estimator (SLWE) at each time instant as a new labeled instance arrives. In this way, the data statistics are updated every time a new element is inserted, without requiring that we have to rebuild its model when changes occur in the data distributions. Finally, and most impo…
Efficient linear fusion of partial estimators
2018
Abstract Many signal processing applications require performing statistical inference on large datasets, where computational and/or memory restrictions become an issue. In this big data setting, computing an exact global centralized estimator is often either unfeasible or impractical. Hence, several authors have considered distributed inference approaches, where the data are divided among multiple workers (cores, machines or a combination of both). The computations are then performed in parallel and the resulting partial estimators are finally combined to approximate the intractable global estimator. In this paper, we focus on the scenario where no communication exists among the workers, de…