Liver cancer leads to more number of human deaths nowadays. Patient survival chances can be increased by early detection of the tumour. Texture analysis based on moment features for CT liver scan images is proposed here. The texture feature is extracted by local binary pattern and statistical features are extracted by Legendre moments. This communication presents a comparative analysis between ...

Non-linear evolution of cosmological energy density fluctuations triggers deviations from Gaussianity in the temperature distribution of the cosmic microwave background. A method to estimate these deviations is proposed. N-body simulations – in a ΛCDM cosmology – are used to simulate the strongly non-linear evolution of cosmological structures. It is proved that these simulations can be combine...

Probability models on graphs are becoming increasingly important in many applications, but statistical tools for fitting such models are not yet well developed. Here we propose a general method of moments approach that can be used to fit a large class of probability models through empirical counts of certain patterns in a graph. We establish some general asymptotic properties of empirical graph...

This paper presents a new approach, based on polynomial optimization and the method of moments, to the problem of anomaly detection. The proposed technique only requires information about the statistical moments of the normal-state distribution of the features of interest and compares favorably with existing approaches (such as Parzen windows and 1-class SVM). In addition, it provides a succinc...

Significant nonlinearities are found in several cyclical components macroeconomic time series across countries. Standard equilibrium models of business cycles successfully explain most first and second moments of these time series. However, this paper shows that a model of this class cannot replicate nonlinear features of the data. Applying the Efficient Method of Moments methodology to build a...

The generalized method of moments (GMM) is a very popular estimation and inference procedure based on moment conditions. When likelihood-based methods are difficult to implement, one can often derive various moment conditions and construct the GMM objective function. However, minimization of the objective function in the GMM may be challenging, especially over a large parameter space. Due to th...

2. In practice, researchers find it useful that GMM estimators can be constructed without specifying the full data generating process (which would be required to write down the maximum likelihood estimator.) This has been the case in the study of single equations in a simultaneous system, in the study of potentially misspecified dynamic models designed to match target moments, and in the constr...

Wear-type rail corrugation is a significant problem in the railway transport industry. Some recent work has suggested that speed control can be used as an effective tool to minimize the rate of corrugation growth. This has brought about the need to model corrugation growth under variable passing speed. Variable speed rail corrugation growth modelling normally consists of either numerical simula...

We extend the general framework of the Multilevel Monte Carlo method to multilevel estimation of arbitrary order central statistical moments. In particular, we prove that under certain assumptions, the total cost of a MLMC central moment estimator is asymptotically the same as the cost of the multilevel sample mean estimator and thereby is asymptotically the same as the cost of a single determi...

The impact of the third (skewness) and fourth (kurtosis) reduced centered moments on the statistical modeling of E1 lines in complex atomic spectra is investigated through the use of Gram-Charlier, Normal Inverse Gaussian and Generalized Gaussian distributions. It is shown that the modeling of unresolved transition arrays with nonGaussian distributions may reveal more detailed structures, due e...