Parametric and non-parametric estimates of willingness to pay for forest recreation in Northern Ireland: A discrete choice contingent valuation study with follow-ups

In this study we show that forest areas contribute significantly to the estimated benefits from om outdoor recreation in Northern Ireland. Secondly we provide empirical evidence of the gains in the statistical efficiency of both benefit and parameter estimates obtained by analysing follow-up responses with Double Bounded interval data analysis. As these gains are considerable, it is clearly worth considering this method in CVM survey design even when moderately large sample sizes are used. Finally we demonstrate that estimates of means and medians of WTP distributions for access to forest recreation show plausible magnitude, are consistent with previous UK studies, and converge across parametric and non-parametic methods of estimation.

Non-parametric nonlinear system identification: A data-driven orthogonal basis function approach

In this paper, a data driven orthogonal basis function approach is proposed for non-parametric FIR nonlinear system identification. The basis functions are not fixed a priori and match the structure of the unknown system automatically. This eliminates the problem of blindly choosing the basis functions without a priori structural information. Further, based on the proposed basis functions, approaches are proposed for model order determination and regressor selection along with their theoretical justifications. © 2008 IEEE.

Kernel based approaches to local nonlinear non-parametric variable selection

In this paper, we consider the variable selection problem for a nonlinear non-parametric system. Two approaches are proposed, one top-down approach and one bottom-up approach. The top-down algorithm selects a variable by detecting if the corresponding partial derivative is zero or not at the point of interest. The algorithm is shown to have not only the parameter but also the set convergence. This is critical because the variable selection problem is binary, a variable is either selected or not selected. The bottom-up approach is based on the forward/backward stepwise selection which is designed to work if the data length is limited. Both approaches determine the most important variables locally and allow the unknown non-parametric nonlinear system to have different local dimensions at different points of interest. Further, two potential applications along with numerical simulations are provided to illustrate the usefulness of the proposed algorithms.

On Variable Selection of a Nonlinear Non-parametric System with a Limited Data Set: A Stepwise Algorithm

his paper considers a problem of identification for a high dimensional nonlinear non-parametric system when only a limited data set is available. The algorithms are proposed for this purpose which exploit the relationship between the input variables and the output and further the inter-dependence of input variables so that the importance of the input variables can be established. A key to these algorithms is the non-parametric two stage input selection algorithm.

Non-parametric bill of quantities estimation of concrete road bridges' superstructure: an artificial neural networks approach

Bridge construction responds to the need for environmentally friendly design of motorways and facilitates the passage through sensitive natural areas and the bypassing of urban areas. However, according to numerous research studies, bridge construction presents substantial budget overruns. Therefore, it is necessary early in the planning process for the decision makers to have reliable estimates of the final cost based on previously constructed projects. At the same time, the current European financial crisis reduces the available capital for investments and financial institutions are even less willing to finance transportation infrastructure. Consequently, it is even more necessary today to estimate the budget of high-cost construction projects -such as road bridges- with reasonable accuracy, in order for the state funds to be invested with lower risk and the projects to be designed with the highest possible efficiency. In this paper, a Bill-of-Quantities (BoQ) estimation tool for road bridges is developed in order to support the decisions made at the preliminary planning and design stages of highways. Specifically, a Feed-Forward Artificial Neural Network (ANN) with a hidden layer of 10 neurons is trained to predict the superstructure material quantities (concrete, pre-stressed steel and reinforcing steel) using the width of the deck, the adjusted length of span or cantilever and the type of the bridge as input variables. The training dataset includes actual data from 68 recently constructed concrete motorway bridges in Greece. According to the relevant metrics, the developed model captures very well the complex interrelations in the dataset and demonstrates strong generalisation capability. Furthermore, it outperforms the linear regression models developed for the same dataset. Therefore, the proposed cost estimation model stands as a useful and reliable tool for the construction industry as it enables planners to reach informed decisions for technical and economic planning of concrete bridge projects from their early implementation stages.

Fracture Mechanics Analysis of the Dentine-Luting Cement Interface.

The objectives of this study were to determine the fracture toughness of adhesive interfaces between dentine and clinically relevant, thin layers of dental luting cements. Cements tested included a conventional glass-ionomer, F (Fuji I), a resin-modified glass-ionomer, FP (Fuji Plus) and a compomer cement, D (DyractCem). Ten miniature short-bar chevron notch specimens were manufactured for each cement, each comprising a 40 µm thick chevron of lute, between two 1.5 mm thick blocks of bovine dentine, encased in resin composite. The interfacial KIC results (MN/m3/2) were median (range): F; 0.152 (0.14-0.16), FP; 0.306 (0.27-0.37), D; 0.351 (0.31-0.37). Non-parametric statistical analysis showed that the fracture toughness of F was significantly lower (p

Significant correlation in linkage signals from genome-wide scans of schizophrenia and schizotypy

Prior family and adoption studies have suggested a genetic relationship between schizophrenia and schizotypy. However, this has never been verified using linkage methods. We therefore attempted to test for a correlation in linkage signals from genome-wide scans of schizophrenia and schizotypy. The Irish study of high-density schizophrenia families comprises 270 families with at least two members with schizophrenia or poor-outcome schizoaffective disorder (n = 637). Non-psychotic relatives were assessed using the structured interview for schizotypy (n = 746). A 10-cM multipoint, non-parametric, autosomal genomewide scan of schizophrenia was performed in Merlin. A scan of a quantitative trait comprising ratings of DSM-III-R criteria for schizotypal personality disorder in non-psychotic relatives was also performed. Schizotypy logarithm of the odds (LOD) scores were regressed onto schizophrenia LOD scores at all loci, with adjustment for spatial autocorrelation. To assess empirical significance, this was also carried out using 1000 null scans of schizotypy. The number of jointly linked loci in the real data was compared to distribution of jointly linked loci in the null scans. No markers were suggestively linked to schizotypy based on strict Lander Kruglyak criteria. Schizotypy LODs predicted schizophrenia LODs above chance expectation genome wide (empirical P = 0.04). Two and four loci yielded nonparametric LOD (NPLs) > 1.0 and > 0.75, respectively, for both schizophrenia and schizotypy (genome-wide empirical P = 0.04 and 0.02, respectively). These results suggest that at least a subset of schizophrenia susceptibility genes also affects schizotypy in non-psychotic relatives. Power may therefore be increased in molecular genetic studies of schizophrenia if they incorporate measures of schizotypy in non-psychotic relatives.

An efficient particle filter for color-based tracking in complex scenes

In this paper, we introduce an efficient method for particle selection in tracking objects in complex scenes. Firstly, we improve the proposal distribution function of the tracking algorithm, including current observation, reducing the cost of evaluating particles with a very low likelihood. In addition, we use a partitioned sampling approach to decompose the dynamic state in several stages. It enables to deal with high-dimensional states without an excessive computational cost. To represent the color distribution, the appearance of the tracked object is modelled by sampled pixels. Based on this representation, the probability of any observation is estimated using non-parametric techniques in color space. As a result, we obtain a Probability color Density Image (PDI) where each pixel points its membership to the target color model. In this way, the evaluation of all particles is accelerated by computing the likelihood p(z|x) using the Integral Image of the PDI.

Lateralisation of language function in young adults born very preterm

Objective: To explore, using functional magnetic resonance imaging (MRI), the functional organisation of phonological processing in young adults born very preterm.

Subjects: Six right handed male subjects with radiological evidence of thinning of the corpus callosum were selected from a cohort of very preterm subjects. Six normal right handed male volunteers acted as controls.

Method: Blood oxygenation level dependent contrast echoplanar images were acquired over five minutes at 1.5 T while subjects performed the tasks. During the ON condition, subjects were visually presented with pairs of non-words and asked to press a key when a pair of words rhymed (phonological processing). This task alternated with the OFF condition, which required subjects to make letter case judgments of visually presented pairs of consonant letter strings (orthographic processing). Generic brain activation maps were constructed from individual images by sinusoidal regression and non-parametric testing. Between group differences in the mean power of experimental response were identified on a voxel wise basis by analysis of variance.

Results: Compared with controls, the subjects with thinning of the corpus callosum showed significantly reduced power of response in the left hemisphere, including the peristriate cortex and the cerebellum, as well as in the right parietal association area. Significantly increased power of response was observed in the right precentral gyrus and the right supplementary motor area.

Conclusions: The data show evidence of increased frontal and decreased occipital activation in male subjects with neurodevelopmental thinning of the corpus callosum, which may be due to the operation of developmental compensatory mechanisms.

A new method for estimating liquidity risk: Insights from a liquidity-adjusted CAPM framework

This paper proposes a new non-parametric method for estimating model-free, time-varying liquidity betas which builds on realized covariance and volatility theory. Working under a liquidity-adjusted CAPM framework we provide evidence that liquidity risk is a factor priced in the Greek stock market, mainly arising from the covariation of individual liquidity with local market liquidity, however, the level of liquidity seems to be an irrelevant variable in asset pricing. Our findings provide support to the notion that liquidity shocks transmitted across securities can cause market-wide effects and can have important implications for portfolio diversification strategies. ©2012 Elsevier B.V. All rights reserved.

Nonparametric Estimation of Data Dimensionality Prior to Data Compression: the case of the Human Development Index

In many applications in applied statistics researchers reduce the complexity of a data set by combining a group of variables into a single measure using factor analysis or an index number. We argue that such compression loses information if the data actually has high dimensionality. We advocate the use of a non-parametric estimator, commonly used in physics (the Takens estimator), to estimate the correlation dimension of the data prior to compression. The advantage of this approach over traditional linear data compression approaches is that the data does not have to be linearized. Applying our ideas to the United Nations Human Development Index we find that the four variables that are used in its construction have dimension three and the index loses information.

A potential vulnerability locus for schizophrenia on chromosome 6p24-22:evidence for genetic heterogeneity

In 265 Irish pedigrees, with linkage analysis we find evidence for a vulnerability locus for schizophrenia in region 6p24-22. The greatest lod score, assuming locus heterogeneity, is 3.51 (P = 0.0002) with D6S296. Another test, the C test, also supported linkage, the strongest results being obtained with D6S296 (P = 0.00001), D6S274 (P = 0.004) and D6S285 (P = 0.006). Non-parametric analysis yielded suggestive, but substantially weaker, findings. This locus appears to influence the vulnerability to schizophrenia in roughly 15 to 30% of our pedigrees. Evidence for linkage was maximal using an intermediate phenotypic definition and declined when this definition was narrowed or was broadened to include other psychiatric disorders.