analysis of messy data volume iii analysis of covariance analysis of messy data, volume iii: analysis of covariance analysis of messy data v 1 haow analysis of messy data volume iii analysis of covariance analysis of messy data volume iii analysis of covariance analysis of messy data volume i designed experiments analysis of messy data volume i designed experiments analysis of messy data ii non...

Liu, Cheng Ph.D., Purdue University, December 2013. Non-parametric Spatial Models. Major Professor: Hao Zhang. Covariance functions play a central role in spatial statistics. Parametric covariance functions have been used in most of the existing works on the analysis of spatial data. The primary reason for this is that the classes of parametric covariance functions guarantee that the fitted cov...

Covariance functions and variograms play a fundamental role in exploratory analysis and statistical modelling of spatial and spatio-temporal datasets. In this paper, we construct a new class of spatial covariance functions using the Fourier transform of some higher-order kernels. Moreover, we extend this class of spatial covariance functions to the spatio-temporal setting using the idea used in...

Spatial firing fields (place fields) of rat hippocampal cells undergo changes when the rat runs stereotyped routes. Previously, Mehta et al. [Experience-dependent asymmetric shape of hippocampal receptive fields, Neuron 25 (2000) 700–715] indicated that spike timingdependent plasticity (STDP) might explain the observed shift of the place field center of mass and the development of skewness. In ...

The optical sensor is composed of two eyesafe parallel laser beams operating at two different wavelengths (see Figure 1). When a vehicle passes in front of the sensor, the laser light is diffused on the surface of the vehicle. The diffused light is then focused on two photodetectors, one for each laser. We thus obtain two signals, x(t) and yet), which represent the fluctuations in time of the i...

Estimating gene networks in combination with posthoc analysis based on data from malignant tissue is a major challenge in cancer systems biology as it allows us to improve our understanding of disease pathology and eventually identify new drug targets. Motivated by the need for improving the inherently unstable covariance estimation compounded by noisy gene expression data, we present a hierarc...