Discussion of "Feature Matching in Time Series Modeling" by Y. Xia and H. Tong

Discussion of "Feature Matching in Time Series Modeling" by Y. Xia and H. Tong [arXiv:1104.3073]Comment: Published in at http://dx.doi.org/10.1214/11-STS345A the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Asymptotic Theory for Clustered Samples

We provide a complete asymptotic distribution theory for clustered data with a large number of independent groups, generalizing the classic laws of large numbers, uniform laws, central limit theory, and clustered covariance matrix estimation. Our theory allows for clustered observations with heterogeneous and unbounded cluster sizes. Our conditions cleanly nest the classical results for i.n.i.d. observations, in the sense that our conditions specialize to the classical conditions under independent sampling. We use this theory to develop a full asymptotic distribution theory for estimation based on linear least-squares, 2SLS, nonlinear MLE, and nonlinear GMM

Antifungal activity of some New Zealand fungal isolates

The secondary metabolism of organisms, especially mycelial fungi, is attracting increasing attention since it may produce a reservoir of unique molecules from which therapeutic agents are formed or derived for clinical application The production of secondary metabolites is not well understood and involves a broad spectrum of metabolic processes that often have little in common. This study screened a number of New Zealand isolates, mostly of the Arthrinium genus, and assessed their antifungal activity. The primary screening used for this study consisted of agar diffusion tests with a range of filamentous fungi and yeast. Secondary screening was also started, with active cultures being chemically extracted. It was found that New Zealand isolates of Arthrinium phaeospermum display antifungal activity against yeasts and filamentous fungi, as well as bacteria. The teleomorphic state of an Arthrinium sp., Apiospora montagnei, was found to have activity against the yeast S. cerevisiae. In addition, activity against two filamentous fungi by Apiospora montagnei was demonstrated. In the course of this study, a contaminant fungus displaying antifungal activity was isolated and screened. It was found to have antifungal activity against C. albicans and filamentous fungi, as well as bacteria

Antifungal activity of some New Zealand fungal isolates

The secondary metabolism of organisms, especially mycelial fungi, is attracting increasing attention since it may produce a reservoir of unique molecules from which therapeutic agents are formed or derived for clinical application The production of secondary metabolites is not well understood and involves a broad spectrum of metabolic processes that often have little in common. This study screened a number of New Zealand isolates, mostly of the Arthrinium genus, and assessed their antifungal activity. The primary screening used for this study consisted of agar diffusion tests with a range of filamentous fungi and yeast. Secondary screening was also started, with active cultures being chemically extracted. It was found that New Zealand isolates of Arthrinium phaeospermum display antifungal activity against yeasts and filamentous fungi, as well as bacteria. The teleomorphic state of an Arthrinium sp., Apiospora montagnei, was found to have activity against the yeast S. cerevisiae. In addition, activity against two filamentous fungi by Apiospora montagnei was demonstrated. In the course of this study, a contaminant fungus displaying antifungal activity was isolated and screened. It was found to have antifungal activity against C. albicans and filamentous fungi, as well as bacteria

Human visual processing of orientation in broad stimuli.

Recently our lab has shown that with broadband stimuli (either visual noise or natural scenes), performance for detecting oriented content is worst at horizontal, best at the obliques, and intermediate at vertical orientations--an anisotropy (termed the horizontal effect ) quite different from the well-known oblique effect (worst performance obliques) obtained with simple line or grating stimuli. This horizontal effect can be explained by a proposed anisotropic normalization model that operates at the level of striate cortex by implementing the known numerical biases of striate neurons preferring different orientations as well as the strength of those responses from neurons tuned to similar orientations and spatial frequencies (with that strength being dependent on the spatial relationships between different scales and orientations present in the stimuli). To assess how the proposed striate normalization mechanism might operate when the visual system is presented with broadband stimuli containing different amounts of spatial frequency and orientation content, two suprathreshold matching experiments were conducted. Additionally, to provide an estimate of how broadband stimuli might modulate the weights of the proposed model, a series of neural response simulations were carried out on different types of broadband natural scene imagery. The stimuli for the psychophysical experiments were generated by making broadband isotropic visual noise patterns and filtering their amplitude spectra to contain a test increment across a specified range of orientations and spatial frequencies. The extent of the test increment\u27s orientation and frequency bandwidth was systematically varied. A standard psychophysical matching paradigm was used to assess the perceived strength of the oriented structure in a test pattern relative to the oriented structure in a comparison pattern. The results yielded the traditional oblique effect when a fairly small range of orientations and high spatial frequencies were incremented and the horizontal effect was observed for broadband increments of about 20 degrees and 1-octave in frequency and larger. A blend of the two anisotropies was observed at intermediate increment bandwidth. The results of the psychophysical experiments were discussed in the context of the proposed striate normalization model with added insight from the results of the neural response simulations

Non-Parametric Data Dependent Bootstrap for Conditional Moment Model

A new non-parametric bootstrap is introduced for dependent data. The bootstrap is based on a weighted empirical-likelihood estimate of the one-step-ahead conditional distribution, imposing the conditional moment restrictions implied by the model. This is the first dependent-data bootstrap procedure which imposes conditional moment restrictions on a bootstrap distribution. The method can be applied to form confidence intervals and p-values from hypothesis tests in Generalized Method of Moments estimation The bootstrap method is illustrated with an application to autoregressive models with martingale difference errors.

Asymptotic Moments of Autoregressive Estimates with a Near Unit Root and Minimax Risk

This moments of the asymptotic distribution of the least-squares estimator of the local-to-unity autoregressive model are computed using computationally simple integration. These calculations show that conventional simulation estimation of moments can be substantially inaccurate unless thesimulationsamplesizeisverylarge. Wealsoexploretheminimaxefficiency of autoregressive coefficient estimation, and numerically show that a simple Stein shrinkage estimator has minimax risk which is uniformly better than least squares, even though the estimation dimension is just one

The lichen genus Hypogymnia in Greenland

Six species of Hypogymnia are reported from Greenland. Hypogymnia incurvoides is added to the lichen flora of the area. Morphology, chemistry, distribution, habitat and ecology are discussed, and a key to the species is presented. Distribution maps of the species are given.

Disentangling the Independent Contributions of Visual and Conceptual Features to the Spatiotemporal Dynamics of Scene Categorization

Human scene categorization is characterized by its remarkable speed. While many visual and conceptual features have been linked to this ability, significant correlations exist between feature spaces, impeding our ability to determine their relative contributions to scene categorization. Here, we used a whitening transformation to decorrelate a variety of visual and conceptual features and assess the time course of their unique contributions to scene categorization. Participants (both sexes) viewed 2250 full-color scene images drawn from 30 different scene categories while having their brain activity measured through 256-channel EEG. We examined the variance explained at each electrode and time point of visual event-related potential (vERP) data from nine different whitened encoding models. These ranged from low-level features obtained from filter outputs to high-level conceptual features requiring human annotation. The amount of category information in the vERPs was assessed through multivariate decoding methods. Behavioral similarity measures were obtained in separate crowdsourced experiments. We found that all nine models together contributed 78% of the variance of human scene similarity assessments and were within the noise ceiling of the vERP data. Low-level models explained earlier vERP variability (88 ms after image onset), whereas high-level models explained later variance (169 ms). Critically, only high-level models shared vERP variability with behavior. Together, these results suggest that scene categorization is primarily a high-level process, but reliant on previously extracted low-level features