We propose a new approach, the calibrated nonparametric scan statistic (CNSS), for more accurate detection of anomalous patterns in large-scale, real-world graphs. Scan statistics identify connected subgraphs that are interesting or unexpected through maximization likelihood ratio statistic; particular, (NPSSs) with higher than expected proportion individually significant nodes. However, we sho...

In breeding for plant disease resistance programs, a large number of new improved genotypes are tested over a range of test pathogens or environments and the underlying statistics used to model this system may be rather complicated. Due to ordinal nature of most measured traits of disease responses, some nonparametric methods used for analyzing genotype × environment (GE) interaction in two dat...

Recent work on nonparametric identification of average partial effects (APEs) from panel data require restrictions on individual or time heterogeneity. Identifying assumptions under the “generalized first-differencing” category, such as time homogeneity (Chernozhukov, Fernandez-Val, Hahn, and Newey, 2013), have testable equality restrictions on the distribution of the outcome variable. This pap...

We present novel methodology to assess undergraduate students’ performance. The proposed methods are based on measures of diversity and on the decomposability of quasi U-statistics to define average distances between and within groups. They have been employed as an alternative to the classic analysis of variance especially when the assumption of normality is not met. The quasi U-statistics nonp...

ii c 2013 Yi Liu ALL RIGHTS RESERVED iii ABSTRACT YI LIU. Generalized Quasi-Likelihood Ratio Statistics for Multivariate Time-varying Coefficient Regression Models. (Under the direction of DR. JIANCHENG JIANG) Generalized likelihood ratio statistics have been a generally applicable method for testing nonparametric hypotheses about nonparametric functions.It has been widely used in many research...

This paper considers a class of nonparametric autoregressive processes and then a class of nonparametric time series regression models with a nonstationary regressor. For the autoregression case, we propose a nonparametric unit–root test for the conditional mean. For the nonparametric time series regression case, we construct a nonparametric test for testing whether the regression is of a known...

This paper considers a class of nonparametric autoregressive processes and then a class of nonparametric time series regression models with a nonstationary regressor. For the autoregression case, we propose a nonparametric unit–root test for the conditional mean. For the nonparametric time series regression case, we construct a nonparametric test for testing whether the regression is of a known...