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The t-distribution (univariate and multivariate) has many useful applications in robust statistical analysis. The parameter estimation of the t-distribution is carried out using maximum likelihood (ML) estimation method, and the ML estimates are obtained via the Expectation-Maximization (EM) algorithm. In this article, we will use the maximum Lq-likelihood (MLq) estimation method introduced by Ferrari and Yang (2010) to estimate all the parameters of the multivariate t-distribution. We modify the EM algorithm to obtain the MLq estimates. We provide a simulation study and a real data example to illustrate the performance of the MLq estimators over the ML estimators. [ABSTRACT FROM AUTHOR]

The papers in this special issue cover a wide range of areas in the methodology and application of spatial econometrics. The first develops a generalized method of moments (GMM) estimator for the spatial regression model from a second-order approximation to the maximum likelihood (ML). The second develops Bayesian estimation in a stochastic frontier model with network dependence in efficiencies, with application to industry dynamics. The third studies cross-country convergence under the Lotka-Volterra model and obtains new insights into spatial spillovers. The penultimate paper develops robust specification tests for the social interactions model under both ML and GMM frameworks. The final paper proposes identification and GMM estimation in a high-order spatial autoregressive model with heterogeneity, common factors and spatial error dependence. [ABSTRACT FROM AUTHOR]

Cooray and Ananda introduced a two-parameter generalized Half-Normal distribution which is useful for modelling lifetime data, while its maximum likelihood estimators (MLEs) are biased in finite samples. This motivates us to construct nearly unbiased estimators for the unknown parameters of the model. In this paper, we adopt two approaches for bias reduction of the MLEs of the parameters of generalized Half-Normal distribution. The first approach is the analytical methodology suggested by Cox and Snell and the second is based on parametric Bootstrap resampling method. Additionally, the method of moments (MMEs) is used for comparison purposes. The numerical evidence shows that the analytic bias-corrected estimators significantly outperform their bootstrapped-based counterpart for small and moderate samples as well as for MLEs and MMEs. Also, it is apparent from the results that bias- corrected estimates of shape parameter perform better than that of scale parameter. Further, the results show that bias-correction scheme yields nearly unbiased estimates. Finally, six fracture toughness real data sets illustrate the application of our methods. [ABSTRACT FROM PUBLISHER]

This article presents a field programmable gate array design and implementation of particle swarm optimisation (PSO) algorithm for multiple input multiple output (MIMO) detection systems. Although, the simulations results prove that the maximum likelihood (ML) detector presents better performances than the other detectors like zero forcing (ZF) and minimum mean square error (MMSE), but with MIMO systems this algorithm has a high computational complexity. To minimise this effect, we apply a new type of cost function and give an efficient calculation algorithm. We propose a PSO approach based on signal transmitted vectorfrom,and. The simulation result shows the effectiveness of the proposed algorithm in terms of achieving better bit error rate performance compared to ZF and MMSE with low complexity. This algorithm has been developed to reduce the ML detector complexity. [ABSTRACT FROM PUBLISHER]

Detailed forest-cover mapping at a regional scale by supervised classification is technically limited by various factors. This study evaluates the ability of a landscape stratification method to improve classification accuracy. An object-based segmentation technique (OBIA) was performed to delineate radiometrically homogeneous regions into the study area, used as strata for the classification of a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data. As a reduction of the spatial variability of the signatures of the vegetation classes is expected, Maximum Likelihood Classifier (MLC) was used to analyse potential effects on classification accuracy. Accuracy assessment was based on the calculation of kappa coefficient (κ) and reject fraction (RF). The values obtained with and without stratification were compared, to assess their global and per-stratum influence on the quality of a detailed forest-cover map (20 different classes). To study the influence of topographical and landscape stratum characteristics on classification accuracy, eight indicators were calculated. Their correlation with κ and RF differences due to stratification was analysed. Our study showed that stratification improved global and per-stratum classification accuracy and in parallel caused an RF increase. Both these evolutions are not conditioned by the stratum topographical and landscape characteristics but strongly influenced by stratum and classified vegetation area. [ABSTRACT FROM AUTHOR]

This article discusses the use of spaceborne polarimetric L-band and C-band synthetic aperture radar (SAR) data for sea-ice detection and classification. The benefits of combining L-band with C-band polarimetric quantities for supervised sea-ice classification in the Eastern Weddell Sea, Antarctica, are investigated. In the experiments, we compared the performance of a maximum likelihood (ML) classifier when used with the combined preferred polarimetric parameters and the individual ones, respectively. The relation between the classification accuracy and the preferred number of polarimetric parameters for classification was examined as well as whether principal component analysis (PCA) and locally linear embedding (LLE) can be used to reduce the dimensionality of the parameter sets. Combining dual-frequency polarimetric quantities improves classification accuracy compared to using individual single-frequency polarimetric quantities. By increasing the dimensionality of the preferred polarimetric parameter sets, the classification using high dimensionality can either result in improvements over the smaller subsets or result in no significant differences. Therefore, using all available polarimetric quantities over the study region is recommended. Further, data fusion with a PCA-based approach is found to be beneficial for sea-ice detection and classification, and poor results have been produced with an LLE-based approach. [ABSTRACT FROM PUBLISHER]