Using different Pitman-closeness techniques for the linear combination of multivariate forecasts

We specify the Pitman-closeness criterion for the evaluation of multivariate forecasts in three categories. This is done closely to the definition of covariance adjustment techniques analysed in other articles. We also apply the Pitman-closeness techniques to an example dealing with German economic data

Hits-and-misses for the evaluation and combination of forecasts

Error measures for the evaluation of forecasts are usually based on the size of the forecast errors. Common measures are e.g. the Mean Squared Error (MSE), the Mean Absolute Deviation (MAD) or the Mean Absolute Percentage Error (MAPE). Alternative measures for the comparison of forecasts are turning points or hits-and-misses, where an indicator loss function is used to decide, if a forecast is of high quality or not. Here, we discuss the latter to obtain reliable combined forecasts

The shrinkage approach in the combination of forecasts

An unbiased point estimator T for an unknown parameter q can be improved in the sense of the Mean Squared Error (MSE) by T T l= l for suitable factors l. Here, we want to discuss this approach in the context of combination of forecasts. We consider the shrinkage technique for unbiased univariate and multivariate forecast combinations. In the univariate case our aim is to reduce the MSE. In the multivariate case we want to improve unbiased forecast combinations in the sense of the Scalar Mean Squared Error (SMSE) or the Matrix Mean Squared Error (MMSE)

Analysis of the Relationship Between Vehicle Weight/Size and Safety, and Implications for Federal Fuel Economy Regulation

This report analyzes the relationship between vehicle weight, size (wheelbase, track width, and their product, footprint), and safety, for individual vehicle makes and models. Vehicle weight and footprint are correlated with a correlation coefficient (R{sup 2}) of about 0.62. The relationship is stronger for cars (0.69) than for light trucks (0.42); light trucks include minivans, fullsize vans, truck-based SUVs, crossover SUVs, and pickup trucks. The correlation between wheelbase and track width, the components of footprint, is about 0.61 for all light vehicles, 0.62 for cars and 0.48 for light trucks. However, the footprint data used in this analysis does not vary for different versions of the same vehicle model, as curb weight does; the analysis could be improved with more precise data on footprint for different versions of the same vehicle model. Although US fatality risk to drivers (driver fatalities per million registered vehicles) decreases as vehicle footprint increases, there is very little correlation either for all light vehicles (0.01), or cars (0.07) or trucks (0.11). The correlation between footprint and fatality risks cars impose on drivers of other vehicles is also very low (0.01); for trucks the correlation is higher (0.30), with risk to others increasing as truck footprint increases. Fatality risks reported here do not account for differences in annual miles driven, driver age or gender, or crash location by vehicle type or model. It is difficult to account for these factors using data on national fatal crashes because the number of vehicles registered to, for instance, young males in urban areas is not readily available by vehicle type or model. State data on all police-reported crashes can be used to estimate casualty risks that account for miles driven, driver age and gender, and crash location. The number of vehicles involved in a crash can act as a proxy of the number of miles a given vehicle type, or model, is driven per year, and is a preferable unit of exposure to a serious crash than the number of registered vehicles. However, because there are relatively few fatalities in the states providing crash data, we calculate casualty risks, which are the sum of fatalities and serious or incapacitating injuries, per vehicle involved in a crash reported to the police. We can account for driver age/gender and driving location effects by excluding from analysis crashes (and casualties) involving young males and the elderly, and occurring in very rural or very urban counties. Using state data on all police-reported crashes in five states, we find that excluding crashes involving young male and elderly drivers has little effect on casualty risk; however, excluding crashes that occurred in the most rural and most urban counties (based on population density) increases casualty risk for all vehicle types except pickups. This suggests that risks for pickups are overstated unless they account for the population density of the county in which the crashes occur. After removing crashes involving young males and elderly drivers, and those occurring in the most rural and most urban counties, we find that casualty risk in all light-duty vehicles tends to increase with increasing weight or footprint; however, the correlation (R{sup 2}) between casualty risk and vehicle weight is 0.31, while the correlation with footprint is 0.23. These relationships are stronger for cars than for light trucks. The correlation between casualty risk in frontal crashes and light-duty vehicle wheelbase is 0.12, while the correlation between casualty risk in left side crashes and track width is 0.36. We calculated separately the casualty risks vehicles impose on drivers of the other vehicles with which they crash. The correlation between casualty risk imposed by light trucks on drivers of other vehicles and light truck footprint is 0.15, while the correlation with light truck footprint is 0.33; risk imposed on others increases as light truck weight or footprint increases. Our analysis indicates that, after excluding crashes involving young male and elderly drivers, and crashes in very rural and very urban counties, and accounting for vehicle weight and footprint, sports cars, pickup trucks and truck-based SUVs have higher risk to their drivers than cars, while import luxury cars and crossover SUVs have lower risk to their drivers than cars. Similarly, pickups and sports cars impose a large casualty risk on drivers of other vehicles, after accounting for vehicle weight and footprint. Our analysis suggests that excluding young male and elderly drivers, and crashes in very rural and urban counties, accounting for vehicle weight, footprint, and type explains only about half of the variability in casualty risk to drivers, and to drivers of other vehicles, by vehicle model

An IFN-Associated Cytotoxic Cellular Immune Response against Viral, Self-, or Tumor Antigens Is a Common Pathogenetic Feature in “Interface Dermatitis”

The term “interface dermatitis” (ID) involves a specific histological inflammatory pattern that is characterized by a cytotoxic lymphocytic infiltration and a hydropic degeneration of the basal epidermal layer. ID is typically seen in autoimmune skin disorders such as lichen planus (LP), cutaneous lupus erythematosus (CLE), and may also appear during immune reactions against drugs, viruses, and tumors. Recent studies have shown that the type-I IFN system is involved in cutaneous autoimmune diseases characterized by ID. IFNs induce the expression of proinflammatory cytokines and chemokines, which support the cellular immune response. The role of IFNs in ID is supported by a close morphological association between the expression pattern of IFN-inducible proteins and the distribution of CXCR3+ lymphocytes. The IFN-inducible chemokine CXCL10 is expressed in exactly those areas where cytotoxic lymphocytes invade the basal epidermis and cause keratinocyte death. A similar picture can be found in early herpes simplex viral skin lesions and viral warts, but also in “lichenoid” actinic keratosis and invasive squamous cell carcinoma. These data suggest that ID morphologically reflects a common IFN-driven cytotoxic attack affecting the basal keratinocytes under different conditions, which is important for antiviral and antitumor immune response, but is inappropriately activated in autoimmune skin disorders

Online Drift Compensation for Chemical Sensors Using Estimation Theory

Sensor drift from slowly changing environmental conditions and other instabilities can greatly degrade a chemical sensor\u27s performance, resulting in poor identification and analyte quantification. In the present work, estimation theory (i.e., various forms of the Kalman filter) is used for online compensation of baseline drift in the response of chemical sensors. Two different cases, which depend on the knowledge of the characteristics of the sensor system, are studied. First, an unknown input is considered, which represents the practical case of analyte detection and quantification. Then, the more general case, in which the sensor parameters and the input are both unknown, is studied. The techniques are applied to simulated sensor data, for which the true baseline and response are known, and to actual liquid-phase SH-SAW sensor data measured during the detection of organophosphates. It is shown that the technique is capable of estimating the baseline signal and recovering the true sensor signal due only to the presence of the analyte. This is true even when the baseline drift changes rate or direction during the detection process or when the analyte is not completely flushed from the system

Hierarchical Models, Marginal Polytopes, and Linear Codes

In this paper, we explore a connection between binary hierarchical models, their marginal polytopes and codeword polytopes, the convex hulls of linear codes. The class of linear codes that are realizable by hierarchical models is determined. We classify all full dimensional polytopes with the property that their vertices form a linear code and give an algorithm that determines them.Comment: 18 page

The DSM 5 and the Istanbul Protocol: Diagnosis of psychological sequels of torture

The Manual on Effective Investigation and Documentation of Torture and Other Cruel, Inhuman or Degrading Treatment or Punishment, commonly known as the Istanbul Protocol, is an interdisciplinary standard supported by, among others, the United Nations and the World Medical Association. It aims at aiding the fight against torture by giving clear guidelines to ensure better and more effective assessment of physical and psychological sequels. Mental health is a key aspect of diagnostical assessment and documentation due to the severe and frequently long-lasting impact of torture that often lasts longer than physical sequels. The inclusion of psychological aspects and a psychiatric diagnosis is to be treated as an important obligatory. Care must be taken to avoid common pitfalls. The new and substantial revisions in the frequently used but also criticised Diagnostical and Statistical Manual (DSM) reflect challenges and opportunities in a comprehensive approach to the documentation of torture