A Visually Adaptive Bayesian Model In Wavelet Regression

The implementation of a Bayesian approach to wavelet regression that corresponds to the human visual system is examined. Most existing research in this area assumes non-informative priors, that is, a prior with mean zero. A new way is offered to implement prior information that mimics a visual inspection of noisy data, to obtain a first impression about the shape of the function that results in a prior with non-zero mean. This visually adaptive Bayesian (VAB) prior has a simple structure, intuitive interpretation, and is easy to implement. Skorohod topology is suggested as a more appropriate measure in signal recovering than the commonly used mean-squared error

A new climatology of maximum and minimum temperature (1951–2010) in the Spanish mainland: a comparison between three different interpolation methods

This study presents a new climatology of monthly temperature for mainland Spain (1951–2010), performed with the highest quality and spatially dense, up-to-date monthly temperature dataset available in the study area (MOTEDAS). Three different interpolation techniques were evaluated: the Local Weighted Linear Regression (LWLR), the Regression-Kriging (RK) and the Regression-Kriging with stepwise selection (RKS), a modification of RK. The performances of the different models were evaluated by the leave-one-out validation procedure, comparing the results from the models with the original data and calculating different error measurements. The three techniques performed better for Tmax than for Tmin, and for the cold, rather than warmer months, also at lower altitude than highland areas. The best results were achieved with LWLR applied for the first time on temperatures in the Spanish mainland. This method improved the accuracy of the temperature reconstruction with respect to RK and RKS. We present a collection of Tmax and Tmin monthly charts, using the same temperature legend to prevent any visual bias in the interpretation of the results. The dataset is available upon request

Predictive Coding for Dynamic Visual Processing: Development of Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model

The current paper proposes a novel predictive coding type neural network model, the predictive multiple spatio-temporal scales recurrent neural network (P-MSTRNN). The P-MSTRNN learns to predict visually perceived human whole-body cyclic movement patterns by exploiting multiscale spatio-temporal constraints imposed on network dynamics by using differently sized receptive fields as well as different time constant values for each layer. After learning, the network becomes able to proactively imitate target movement patterns by inferring or recognizing corresponding intentions by means of the regression of prediction error. Results show that the network can develop a functional hierarchy by developing a different type of dynamic structure at each layer. The paper examines how model performance during pattern generation as well as predictive imitation varies depending on the stage of learning. The number of limit cycle attractors corresponding to target movement patterns increases as learning proceeds. And, transient dynamics developing early in the learning process successfully perform pattern generation and predictive imitation tasks. The paper concludes that exploitation of transient dynamics facilitates successful task performance during early learning periods.Comment: Accepted in Neural Computation (MIT press

Impervious surface estimation using remote sensing images and gis : how accurate is the estimate at subdivision level?

Impervious surface has long been accepted as a key environmental indicator linking development to its impacts on water. Many have suggested that there is a direct correlation between degree of imperviousness and both quantity and quality of water. Quantifying the amount of impervious surface, however, remains difficult and tedious especially in urban areas. Lately more efforts have been focused on the application of remote sensing and GIS technologies in assessing the amount of impervious surface and many have reported promising results at various pixel levels. This paper discusses an attempt at estimating the amount of impervious surface at subdivision level using remote sensing images and GIS techniques. Using Landsat ETM+ images and GIS techniques, a regression tree model is first developed for estimating pixel imperviousness. GIS zonal functions are then used to estimate the amount of impervious surface for a sample of subdivisions. The accuracy of the model is evaluated by comparing the model-predicted imperviousness to digitized imperviousness at the subdivision level. The paper then concludes with a discussion on the convenience and accuracy of using the method to estimate imperviousness for large areas

Graphics for uncertainty

Graphical methods such as colour shading and animation, which are widely available, can be very effective in communicating uncertainty. In particular, the idea of a ‘density strip’ provides a conceptually simple representation of a distribution and this is explored in a variety of settings, including a comparison of means, regression and models for contingency tables. Animation is also a very useful device for exploring uncertainty and this is explored particularly in the context of flexible models, expressed in curves and surfaces whose structure is of particular interest. Animation can further provide a helpful mechanism for exploring data in several dimensions. This is explored in the simple but very important setting of spatiotemporal data

Study of Optimal Perimetric Testing In Children (OPTIC): Normative visual field values in children

Purpose: We sought to define normative visual field (VF) values for children using common clinical test protocols for kinetic and static perimetry. Design: Prospective, observational study. Subjects: We recruited 154 children aged 5 to 15 years without any ophthalmic condition that would affect the VF (controls) from pediatric clinics at Moorfields Eye Hospital. Methods: Children performed perimetric assessments in a randomized order using Goldmann and Octopus kinetic perimetry, and Humphrey static perimetry (Swedish Interactive Thresholding Algorithm [SITA] 24-2 FAST), in a single sitting, using standardized clinical protocols, with assessment by a single examiner. Unreliable results (assessed qualitatively) were excluded from the normative data analysis. Linear, piecewise, and quantile mixed-effects regression models were used. We developed a method to display age-specific normative isopters graphically on a VF plot to aid interpretation. Main Outcome Measures: Summary measures and graphical plots describing normative VF data for 3 common perimetric tests. Results: Visual field area increased with age on testing with Goldmann isopters III4e, I4e, and I2e (linear regression; P < 0.001) and for Octopus isopters III4e and I4e (linear regression; P < 0.005). Visual field development occurs predominately in the infero-temporal field. Humphrey mean deviation (MD) showed an increase of 0.3 decibels (dB; 95% CI, 0.21-0.40) MD per year up to 12 years of age, when adult MD values were reached and thereafter maintained. Conclusions: Visual field size and sensitivity increase with age in patterns that are specific to the perimetric approach used. These developmental changes should be accounted for when interpreting perimetric test results in children, particularly when monitoring change over time

Stability

Reproducibility is imperative for any scientific discovery. More often than not, modern scientific findings rely on statistical analysis of high-dimensional data. At a minimum, reproducibility manifests itself in stability of statistical results relative to "reasonable" perturbations to data and to the model used. Jacknife, bootstrap, and cross-validation are based on perturbations to data, while robust statistics methods deal with perturbations to models. In this article, a case is made for the importance of stability in statistics. Firstly, we motivate the necessity of stability for interpretable and reliable encoding models from brain fMRI signals. Secondly, we find strong evidence in the literature to demonstrate the central role of stability in statistical inference, such as sensitivity analysis and effect detection. Thirdly, a smoothing parameter selector based on estimation stability (ES), ES-CV, is proposed for Lasso, in order to bring stability to bear on cross-validation (CV). ES-CV is then utilized in the encoding models to reduce the number of predictors by 60% with almost no loss (1.3%) of prediction performance across over 2,000 voxels. Last, a novel "stability" argument is seen to drive new results that shed light on the intriguing interactions between sample to sample variability and heavier tail error distribution (e.g., double-exponential) in high-dimensional regression models with $p$ predictors and $n$ independent samples. In particular, when $p/n\rightarrow\kappa\in(0.3,1)$ and the error distribution is double-exponential, the Ordinary Least Squares (OLS) is a better estimator than the Least Absolute Deviation (LAD) estimator.Comment: Published in at http://dx.doi.org/10.3150/13-BEJSP14 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm