Ball: An R package for detecting distribution difference and association in metric spaces

The rapid development of modern technology facilitates the appearance of numerous unprecedented complex data which do not satisfy the axioms of Euclidean geometry, while most of the statistical hypothesis tests are available in Euclidean or Hilbert spaces. To properly analyze the data of more complicated structures, efforts have been made to solve the fundamental test problems in more general spaces. In this paper, a publicly available R package Ball is provided to implement Ball statistical test procedures for K-sample distribution comparison and test of mutual independence in metric spaces, which extend the test procedures for two sample distribution comparison and test of independence. The tailormade algorithms as well as engineering techniques are employed on the Ball package to speed up computation to the best of our ability. Two real data analyses and several numerical studies have been performed and the results certify the powerfulness of Ball package in analyzing complex data, e.g., spherical data and symmetric positive matrix data

Competitiveness of dairy farms in three countries: the role of CAP subsidies

This paper investigates the impact of CAP subsidies on the competitiveness of dairy farms in Germany, the Netherlands, and Sweden. Technical efficiency results show that coupled subsidies have negative impacts in Germany and the Netherlands, but no significant impacts in Sweden. Decoupled subsidies negatively affect technical efficiency in each country and to a larger extent than coupled subsidies. Relative productivity results indicate that Dutch technology leads to the highest output, followed by technologies in Germany and Sweden. Dutch farms can improve their competitiveness by exploring their current production potential. Besides improving efficiency, German and Swedish farms may have options to improve their production technology.technical efficiency, output distance function, dairy farm, subsidy, relative productivity, Agricultural and Food Policy, Livestock Production/Industries,

A Splicing Approach to Best Subset of Groups Selection

Best subset of groups selection (BSGS) is the process of selecting a small part of non-overlapping groups to achieve the best interpretability on the response variable. It has attracted increasing attention and has far-reaching applications in practice. However, due to the computational intractability of BSGS in high-dimensional settings, developing efficient algorithms for solving BSGS remains a research hotspot. In this paper,we propose a group-splicing algorithm that iteratively detects the relevant groups and excludes the irrelevant ones. Moreover, coupled with a novel group information criterion, we develop an adaptive algorithm to determine the optimal model size. Under mild conditions, it is certifiable that our algorithm can identify the optimal subset of groups in polynomial time with high probability. Finally, we demonstrate the efficiency and accuracy of our methods by comparing them with several state-of-the-art algorithms on both synthetic and real-world datasets.Comment: 49 pages, 7 figure

Generalized synchronization-based partial topology identification of complex networks

summary:In this paper, partial topology identification of complex networks is investigated based on synchronization method. We construct the response networks consisting of nodes with sim-pler dynamics than that in the drive networks. By constructing Lyapunov function, sufficient conditions are derived to guarantee partial topology identification by designing suitable controllers and parameters update laws. Several numerical examples are provided to illustrate the effectiveness of the theoretical results

Nonparametric statistical inference via metric distribution function in metric spaces

The distribution function is essential in statistical inference and connected with samples to form a directed closed loop by the correspondence theorem in measure theory and the Glivenko-Cantelli and Donsker properties. This connection creates a paradigm for statistical inference. However, existing distribution functions are defined in Euclidean spaces and are no longer convenient to use in rapidly evolving data objects of complex nature. It is imperative to develop the concept of the distribution function in a more general space to meet emerging needs. Note that the linearity allows us to use hypercubes to define the distribution function in a Euclidean space. Still, without the linearity in a metric space, we must work with the metric to investigate the probability measure. We introduce a class of metric distribution functions through the metric only. We overcome this challenging step by proving the correspondence theorem and the Glivenko-Cantelli theorem for metric distribution functions in metric spaces, laying the foundation for conducting rational statistical inference for metric space-valued data. Then, we develop a homogeneity test and a mutual independence test for non-Euclidean random objects and present comprehensive empirical evidence to support the performance of our proposed methods. Supplementary materials for this article are available online

A SIMPLE Approach to Provably Reconstruct Ising Model with Global Optimality

Reconstruction of interaction network between random events is a critical problem arising from statistical physics and politics to sociology, biology, and psychology, and beyond. The Ising model lays the foundation for this reconstruction process, but finding the underlying Ising model from the least amount of observed samples in a computationally efficient manner has been historically challenging for half a century. By using the idea of sparsity learning, we present a approach named SIMPLE that has a dominant sample complexity from theoretical limit. Furthermore, a tuning-free algorithm is developed to give a statistically consistent solution of SIMPLE in polynomial time with high probability. On extensive benchmarked cases, the SIMPLE approach provably reconstructs underlying Ising models with global optimality. The application on the U.S. senators voting in the last six congresses reveals that both the Republicans and Democrats noticeably assemble in each congresses; interestingly, the assembling of Democrats is particularly pronounced in the latest congress

The Impact of Agri-Environmental Policies and Production Intensification on the Environmental Performance of Dutch Dairy Farms

This study examines the impact of policies and intensification on the environmental performance of Dutch dairy farms in the period 2001-2010 using a hyperbolic distance function. The results indicate that the change from the Mineral Accounting System to the combination of the Application Standards Policy with decoupled payments has not significantly changed farms’ hyperbolic efficiency. Farms receiving agri-environmental and animal welfare payments are less hyperbolically efficient than those that do not, highlighting greater decreases in desirable outputs than decreases in undesirable outputs. Finally, intensification increases hyperbolic efficiency, suggesting that intensive practices may increase production without harming the environment

Ball: An R Package for Detecting Distribution Difference and Association in Metric Spaces

The rapid development of modern technology has created many complex datasets in non-linear spaces, while most of the statistical hypothesis tests are only available in Euclidean or Hilbert spaces. To properly analyze the data with more complicated structures, efforts have been made to solve the fundamental test problems in more general spaces (Lyons 2013; Pan, Tian, Wang, and Zhang 2018; Pan, Wang, Zhang, Zhu, and Zhu 2020). In this paper, we introduce a publicly available R package Ball for the comparison of multiple distributions and the test of mutual independence in metric spaces, which extends the test procedures for the equality of two distributions (Pan et al. 2018) and the independence of two random objects (Pan et al. 2020). The Ball package is computationally efficient since several novel algorithms as well as engineering techniques are employed in speeding up the ball test procedures. Two real data analyses and diverse numerical studies have been performed, and the results certify that the Ball package can detect various distribution differences and complicated dependencies in complex datasets, e.g., directional data and symmetric positive definite matrix data