Item reliability is of special interest for Mokken’s nonparametric item response theory, and is useful for the evaluation of item quality in nonparametric test construction research. It is also of interest for nonparametric person-fit analysis. Three methods for the estimation of the reliability of single dichotomous items are discussed. All methods are based on the assumptions of nondecreasing...

This thesis investigates some unaddressed problems in kernel nonparametrichypothesis testing. The contributions are grouped around three main themes:Wild Bootstrap for Degenerate Kernel Tests. A wild bootstrap method for non-parametric hypothesis tests based on kernel distribution embeddings is pro-posed. This bootstrap method is used to construct provably consistent teststh...

KEY FINDINGS Following a long period of fairly steady increase, the U.S. preterm birth rate declined for the second straight year in 2008 to 12.3 percent, from 12.8 percent in 2006. This marks the first 2-year decline in the preterm birth rate in nearly three decades. Preterm birth rates declined from 2006 to 2008 for mothers of all age groups under age 40, for the three largest race and Hispan...

This paper considers the problem of consistent model specification tests using series estimation methods. The null models we consider in this paper all contain some nonparametric components. A leading case we consider is to test for an additive partially linear model. The null distribution of the test statistic is derived using a central limit theorem for Hilbert valued random arrays. The test ...

In this article, we present nonparametric and semiparametric methods to analyze current status data subject to outcome misclassification. Our methods use nonparametric maximum likelihood estimation (NPMLE) to estimate the distribution function of the failure time when sensitivity and specificity are known and may vary among subgroups. A nonparametric test is proposed for the two sample hypothes...

Non-rigid registration of MR images to a common reference image results in deformation fields, from which anatomical differences can be statistically assessed, within and between populations. Without further assumptions, nonparametric tests are required and currently the analysis of deformation fields is performed by permutation tests. For deformation fields, often the vector magnitude is chose...

As an improved method of k-nearest neighbor classification, the local mean-based nonparametric classifier had the ability to resist the effects of noise and classify unbalanced data. When selecting the nearest k samples and calculating the distance between the test samples and the local mean-based vectors, it always used Euclidean distance. However, for multi-dimensional data, using Euclidean d...

We provide a principled framework for nonparametrically learning activation functions in deep neural networks. Currently, state-of-the-art deep networks treat choice of activation function as a hyper-parameter before training. By allowing activation functions to be estimated as part of the training procedure, we expand the class of functions that each node in the network can learn. We also prov...