A Test of U-type for Goodness-of-fit in Regression Models Through Martingale Difference Divergence

Based on the martingale difference divergence,a recently proposed metric for quantifying conditional mean dependence,we introduce a consistent test of U-type for the goodness-of-fit of linear models under conditional mean restriction.Methodologically,our test allows heteroscedastic regression models without imposing any condition on the distribution of the error,utilizes effectively important information contained in the distance of the vector of covariates,has a simple form,is easy to implement,and is free of the subjective choice of parameters.Theoretically,our mathematical analysis is of own interest since it does not take advantage of the empirical process theory and provides some insights on the asymptotic behavior of U-statistic in the framework of model diagnostics.The asymptotic null distribution of the proposed test statistic is derived and its asymptotic power behavior against fixed alternatives and local alternatives converging to the null at the parametric rate is also presented.In particular,we show that its asymptotic null distribution is very different from that obtained for the true error and their differences are interestingly related to the form expression for the estimated parameter vector embodied in regression function and a martingale difference divergence matrix.Since the asymptotic null distribution of the test statistic depends on data generating process,we propose a wild bootstrap scheme to approximate its null distribution.The consistency of the bootstrap scheme is justified.Numerical studies are undertaken to show the good performance of the new test.

Single Image Super-Resolution by Clustered Sparse Representation and Adaptive Patch Aggregation

A Single Image Super-Resolution (SISR) reconstruction method that uses clustered sparse representation and adaptive patch aggregation is proposed. First, we randomly extract image patch pairs from the training images, and divide these patch pairs into different groups by K-means clustering. Then, we learn an over-complete sub-dictionary pair offline from corresponding group patch pairs. For a given low-resolution patch, we adaptively select one sub-dictionary to reconstruct the high resolution patch online. In addition, non-local self-similarity and steering kernel regression constraints are integrated into patch aggregation to improve the quality of the recovered images. Experiments show that the proposed method is able to realize state-of-the-art performance in terms of both objective evaluation and visual perception.

Heteroscedasticity Detection and Estimation with Quantile Difference Method

When dealing with regression analysis,heteroscedasticity is a problem that the authors have to face with.Especially if little information can be got in advance,detection of heteroscedasticity as well as estimation of statistical models could be even more difficult.To this end,this paper proposes a quantile difference method(QDM) that can effectively estimate the heteroscedastic function.This method,being completely free from the estimation of mean regression function,is simple,robust and easy to implement.Moreover,the QDM method enables the detection of heteroscedasticity without any restrictions on error terms,consequently being widely applied.What is worth mentioning is that based on the proposed approach estimators of both mean regression function and heteroscedastic function can be obtained.In the end,the authors conduct some simulations to examine the performance of the proposed methods and use a real data to make an illustration.

Spatial variations of terrain and their impacts on landscape patterns in the transition zone from mountains to plains—A case study of Qihe River Basin in the Taihang Mountains

Terrain plays a key role in landscape pattern formation, particularly in the transition zones from mountains to plains.Exploring the relationships between terrain characteristics and landscape types in terrain-complex areas can help reveal the mechanisms underlying the relationships. In this study, Qihe River Basin, situated in the transition zone from the Taihang Mountains to the North-China Plain, was selected as a case study area. First, the spatial variations in the relief amplitudes(i.e.,high-amplitude terrain undulations) were analyzed. Second, the effects of relief amplitudes on the landscape patterns were indepth investigated from the perspectives of both landscape types and landscape indices. Finally, a logistic regression model was employed to examine the relationships between the landscape patterns and the influencing factors(natural and human) at different relief amplitudes. The results show that with increasing relief amplitude, anthropogenic landscapes gradually give in to natral landscapes. Specifically, human factors normally dominate the gentle areas(e.g., flat areas) in influencing the distribution of landscape types, and natural factors normally dominate the highly-undulating areas(e.g., moderate relief areas). As for the intermediately undulating areas(i.e.,medium relief amplitudes), a combined influence of natural and human factors result in the highest varieties of landscape types. The results also show that in micro-relief areas and small relief areas where natural factors and human factors are more or less equally active,landscape types are affected by a combination of natural and human factors.The combination leads to a high fragmentation and a high diversity of landscape patterns. It seems that appropriate human interferences in these areas can be conducive to enhancing landscape diversity and that inappropriate human interferences can aggravate the problems of landscape fragmentation.

Theoretical prediction and validation of global horizontal solar irradiance for a tropical climate in India

This paper aims to propose monthly models responsible for the theoretical evaluation of the global horizontal irradiance of a tropical region in India which is Sivagangai situated in Tamilnadu. The actual measured global horizontal irradiance hails from a 5 MW solar power plant station located at Sivagangai in Tamilnadu. The data were monitored from May 2011 to April 2013. The theoretical assessment was conducted differently by employing a programming platform called Microsoft Visual Basic 2010 Express. A graphical user interface was created using Visual Basic 2010 Express, which provided the evaluation of empirical parameters for model formulation such as daily sunshine duration （5）, maximum possible sunshine hour duration （S0）, extra terrestrial horizontal global irradiance （H0） and extra terrestrial direct normal irradiance （G0）. The proposed regression models were validated by the significance of statistical indicators such as mean bias error, root mean square error and mean percentage error from the predicted and the actual values for the region considered. Comparison was made between the proposed monthly models and the existing normalized models for global horizontal irradiance evaluation.