Hypothesis testing for band size detection of high-dimensional banded precision matrices

Many statistical analysis procedures require a good estimator for a high-dimensional covariance matrix or its inverse, the precision matrix. When the precision matrix is banded, the Cholesky-based method often yields a good estimator of the precision matrix. One important aspect of this method is determination of the band size of the precision matrix. In practice, crossvalidation is commonly used; however, we show that crossvalidation not only is computationally intensive but can be very unstable. In this paper, we propose a new hypothesis testing procedure to determine the band size in high dimensions. Our proposed test statistic is shown to be asymptotically normal under the null hypothesis, and its theoretical power is studied. Numerical examples demonstrate the effectiveness of our testing procedure

Clustering time series based on dependence structure.

The clustering of time series has attracted growing research interest in recent years. The most popular clustering methods assume that the time series are only linearly dependent but this assumption usually fails in practice. To overcome this limitation, in this paper, we study clustering methods applicable to time series with a general and dependent structure. We propose a copula-based distance to measure dissimilarity among time series and consider an estimator for it, where the strong consistency of the estimator is guaranteed. Once the pairwise distance matrix for time series has been obtained, we apply a hierarchical clustering algorithm to cluster the time series and ensure its consistency. Numerical studies, including a large number of simulations and analysis of practical data, show that our method performs well

Logistic regression with image covariates via the combination of L1 and Sobolev regularizations.

The use of image covariates to build a classification model has lots of impact in various fields, such as computer science, medicine, and so on. The aim of this paper is to develop an estimation method for logistic regression model with image covariates. We propose a novel regularized estimation approach, where the regularization is a combination of L1 regularization and Sobolev norm regularization. The L1 penalty can perform variable selection, while the Sobolev norm penalty can capture the shape edges information of image data. We develop an efficient algorithm for the optimization problem. We also establish a nonasymptotic error bound on parameter estimation. Simulated studies and a real data application demonstrate that our proposed method performs very well

INFORME CAMPAÑA ARSA 0394

Durante los días 28 de Febrero al 8 de Marzo del 1994 se ha llevado a cabo la cuarta de las campañas de la serie "Arrastre Suratlántica" (ARSA 0394). La zona prospectada ha correspondido a la zona de plataforma y talud continental de la parte española del Golfo de Cádiz, comprendida entre el meridiano 7º 20’ W, o la frontera con Portugal, el paralelo 36º 15’ N, entre las isóbatas de 15 y 800 metros, siendo su límite inferior la distancia de 6 millas a la costa. La campaña se realizó a bordo del B/O "Cornide de Saavedra", siendo el objetivo previsto la estimación de los índices de abundancia (número y biomasa), de las especies demersales de mayor interés pesquero, así como de la fauna asociada a ellas