Component Selection in the Additive Regression Model

Similar to variable selection in the linear regression model, selecting significant components in the popular additive regression model is of great interest. However, such components are unknown smooth functions of independent variables, which are unobservable. As such, some approximation is needed. In this paper, we suggest a combination of penalized regression spline approximation and group variable selection, called the lasso-type spline method (LSM), to handle this component selection problem with a diverging number of strongly correlated variables in each group. It is shown that the proposed method can select significant components and estimate nonparametric additive function components simultaneously with an optimal convergence rate simultaneously. To make the LSM stable in computation and able to adapt its estimators to the level of smoothness of the component functions, weighted power spline bases and projected weighted power spline bases are proposed. Their performance is examined by simulation studies across two set-ups with independent predictors and correlated predictors, respectively, and appears superior to the performance of competing methods. The proposed method is extended to a partial linear regression model analysis with real data, and gives reliable results

Methods for the Evaluation of Wingsails with a Crescent-Shaped Profile

Seaborne transportation accounts for a large proportion of greenhouse gas (GHG) emissions. The International Maritime Organization (IMO) has stipulated that GHG emissions should be reduced by 50% before 2050 compared to 2018. The use of wind-assisted ship propulsion (WASP) is considered one of the most effective ways to reduce GHG emissions. Therefore, the present study aims to establish multidisciplinary numerical models for predicting and evaluating the propulsive performance and structural response of WASP systems.\ua0Conceptual designs of a set of telescopic wingsail rigs are generated. Numerical simulations, including computational fluid dynamics (CFD) simulations and finite element analysis, are performed for dimensioning and optimizing wingsail structures for ships to understand the fluid–structural interaction (FSI). Since the deformation of the wingsail structure that the surrounding flow excites is so large, the interaction between the flow and structure creates a coupled problem. Analysis of a crescent-shaped wingsail using an in-house software ShipCLEAN, which is based on a generic ship energy model, is conducted to evaluate this wingsail’s propulsive performance in comparison with other WASP concepts.\ua0It is concluded that wingsails with a sectional profile and significant camber have much better propulsive performance than those with conventional airfoil profiles because the potential thrust force coefficient is approximately 30% higher. It is also found that the external loads on the crescent-shaped wingsail show notable periodic oscillations due to strong flow separation, so it can be inferred that wingsails can suffer from remarkable vortex-induced vibration. This raises higher requirements on the strength and rigidity of the wingsail structure. Tip vortices are found to have negative impacts on thrust, and the sail can strongly influence the wake flow. It is also concluded from the structural analysis that the strength, especially the von Mises yield and compressive normal stress, is most critical among the assessment criteria that are considered when evaluating the wingsail structure. Using a strong frame to bear global bending and introducing a cubic-shaped mast prevents stress concentration and reduces the weight of the structures

Robust rank correlation based screening

Independence screening is a variable selection method that uses a ranking criterion to select significant variables, particularly for statistical models with nonpolynomial dimensionality or "large p, small n" paradigms when p can be as large as an exponential of the sample size n. In this paper we propose a robust rank correlation screening (RRCS) method to deal with ultra-high dimensional data. The new procedure is based on the Kendall \tau correlation coefficient between response and predictor variables rather than the Pearson correlation of existing methods. The new method has four desirable features compared with existing independence screening methods. First, the sure independence screening property can hold only under the existence of a second order moment of predictor variables, rather than exponential tails or alikeness, even when the number of predictor variables grows as fast as exponentially of the sample size. Second, it can be used to deal with semiparametric models such as transformation regression models and single-index models under monotonic constraint to the link function without involving nonparametric estimation even when there are nonparametric functions in the models. Third, the procedure can be largely used against outliers and influence points in the observations. Last, the use of indicator functions in rank correlation screening greatly simplifies the theoretical derivation due to the boundedness of the resulting statistics, compared with previous studies on variable screening. Simulations are carried out for comparisons with existing methods and a real data example is analyzed.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1024 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org). arXiv admin note: text overlap with arXiv:0903.525