This article introduces an informative goodness-of-fit (iGOF) approach to study multivariate distributions. When the null model is rejected, iGOF allows us identify underlying sources of mismodeling and naturally equips practitioners with additional insights on nature deviations from true distribution. The character procedure achieved by exploiting smooth tests random fields theory facilitate a...

Some resularities are more salient to the visual system than others. P. A. van der Helm and E. L. J. Leeuwenberg (1996) have proposed a new approach that quantifies the goodness of a pattern's regularity as the number of holographic identities constituting the regulartiy, relative to the total amount of information needed to describe the pattern. This holographic approach to goodness was compar...

of the variation in the data, and we would disagree that the straight line is “obviously bad” or “not a good description of the data.” We do not advocate elimination of the term “test for the significance of the regression,” but we would like to see the term “goodness of fit” associated with F = MSifMS and dissociated from F = MS101/MSpe. The larger the value of F = MSiact / MS, the greater is ...

Low-rank matrix recovery (LMR) is a rank minimization problem subject to linear equality constraints, and it arises in many fields such as signal and image processing, statistics, computer vision, system identification and control. This class of optimization problems is generally NP-hard. A popular approach replaces the rank function with the nuclear norm of the matrix variable. In this paper, ...

Map building is a classical problem in mobile and autonomous robotics, and sensor models is a way to interpret raw sensory information, especially for building maps. In this paper we propose a parameterized sensor model, and optimize map goodness with respect to these parameters. A new approach, measuring the goodness of maps without a handcrafted map of the actual environment is introduced and...

In this paper, we develop verifiable and computable performance analysis of sparsity recovery. We define a family of goodness measures for arbitrary sensing matrices as a set of optimization problems, and design algorithms with a theoretical global convergence guarantee to compute these goodness measures. The proposed algorithms solve a series of second-order cone programs, or linear programs. ...

Many works have been carried out to compare the efficiency of several goodness of fit procedures for identifying whether or not a particular distribution could adequately explain a data set. In this paper a study is conducted to investigate the power of several goodness of fit tests such as Kolmogorov Smirnov (KS), Anderson-Darling(AD), CramervonMises (CV) and a proposed modification of Kolmogo...