50 resultados para Bayesian p-values


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This paper examines cyclical behaviour in commercial property values over the period 1956 to 1996, using a structural times series (unobserved components) approach. The influence of the transition to short rent reviews during the late 1960s and the short and long-term impacts of the 1974 and 1990 property crashes are also incorporated into the analysis, via dummy variables. It is found that once these variables are taken into account a fairly regular cyclical pattern can be discerned, with a period of about 7.8 years. Furthermore, the 1974 and 1990 property crashes are shown to have had a major long-term impact on property value growth (presumably via their influence on investors' expectations).

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This paper focuses on the effect of energy performance ratings on the capital values, rental values and equivalent yields of UK commercial property assets. Of which a small number are also BREEAM rated, the study is based upon 708 commercial property assets held in the IPD UK Universe drawn from across all PAS segments. Incorporating a range of controls such as unexpired lease term, vacancy rate and tenant credit risk, hedonic regression procedures are used to estimate the effect of EPC rating. The study finds no evidence of a strong relationship between environmental and/or energy performance and rental and capital value. Bearing in mind the small number of BREEAM rated assets, there was a small but statistically significant effect on equivalent yield only. Similarly, there was no evidence that the EPC rating had any effect on Market Rent or Market Value with only minor effects of EPC ratings on equivalent yields. The preliminary conclusion is that energy labelling is not yet having the effects on Market Values and Market Rents that provide incentives for market participants to improve the energy efficiency of their commercial real estate assets.

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Land surface albedo is dependent on atmospheric state and hence is difficult to validate. Over the UK persistent cloud cover and land cover heterogeneity at moderate (km-scale) spatial resolution can also complicate comparison of field-measured albedo with that derived from instruments such as the Moderate Resolution Imaging Spectrometer (MODIS). A practical method of comparing moderate resolution satellite-derived albedo with ground-based measurements over an agricultural site in the UK is presented. Point measurements of albedo made on the ground are scaled up to the MODIS resolution (1 km) through reflectance data obtained at a range of spatial scales. The point measurements of albedo agreed in magnitude with MODIS values over the test site to within a few per cent, despite problems such as persistent cloud cover and the difficulties of comparing measurements made during different years. Albedo values derived from airborne and field-measured data were generally lower than the corresponding satellite-derived values. This is thought to be due to assumptions made regarding the ratio of direct to diffuse illumination used when calculating albedo from reflectance. Measurements of albedo calculated for specific times fitted closely to the trajectories of temporal albedo derived from both Systeme pour l'Observation de la Terre (SPOT) Vegetation (VGT) and MODIS instruments.

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This paper develops an account of the normative basis of priority setting in health care as combining the values which a given society holds for the common good of its members, with the universal provided by a principle of common humanity. We discuss national differences in health basket in Europe and argue that health care decision-making in complex social and moral frameworks is best thought of as anchored in such a principle by drawing on the philosophy of need. We show that health care needs are ethically ‘thick’ needs whose psychological and social construction can best be understood in terms of David Wiggins's notion of vital need: a person's need is vital when failure to meet it leads to their harm and suffering. The moral dimension of priority setting which operates across different societies’ health care systems is located in the demands both of and on any society to avoid harm to its members.

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Numerical Weather Prediction (NWP) fields are used to assist the detection of cloud in satellite imagery. Simulated observations based on NWP are used within a framework based on Bayes' theorem to calculate a physically-based probability of each pixel with an imaged scene being clear or cloudy. Different thresholds can be set on the probabilities to create application-specific cloud-masks. Here, this is done over both land and ocean using night-time (infrared) imagery. We use a validation dataset of difficult cloud detection targets for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) achieving true skill scores of 87% and 48% for ocean and land, respectively using the Bayesian technique, compared to 74% and 39%, respectively for the threshold-based techniques associated with the validation dataset.