A linear forecasting model and its application to economic data

We present a forecasting model based on fuzzy pattern recognition and weighted linear regression. In this model fuzzy pattern recognition is used to find homogeneous fuzzy classes in a heterogeneous data set. It is assumed that the classes represent typical situations. For each class a weighted regression analysis is conducted. The forecasting results obtained by the class regression analysis are aggregated to obtain the ‘overall’ estimation of the regression model. We apply the model to the forecasting of economic data of the USA. Copyright © 2001 John Wiley & Sons, Ltd.     

A technique for repetitive long-term measurement of aortic pressure and cardiac output in the unanaesthetized dog using an implanted catheter

Zusammenfassung Es wird die Herstellung und Implantation von Aortenkathetern beim Hund beschrieben. Die Methode erlaubt wiederholte, kontinuierliche blutige Messung des Aortendrucks sowie die Bestimmung des Herzminutenvolumens am wachen Tier.     

Permeability of arthrodial membrane to water: A first measurement using in vivo techniques

Summary Despite differences in surface morphology and fine structure, the permeabilities of untreated scorpion sternite and pleural cuticle to water are similar (0.69 versus 0.79 g·cm^–2·h^–1·mm Hg^–1). Hexane applied to pleural membrane increased its permeability 9-fold, but neither hexane nor chloroform: methanol had much effect on sternite permeability. When sternite cuticle was treated with 1.0 N KOH followed by chloroform: methanol, permeability increased about three times over control values. In contrast, cockroach pronotum, which is 17 times more permeable than scorpion sternite, exhibited a marked increase in permeability when treated with just hexane. In both the scorpion pleuron and cockroach pronotum, disruption of the lipid barrier caused by rubbing is partially responsible for the higher permeabilities observed following solvent treatment.     

A method for turbidimetric measurement of heparin: Its application to the estimation of heparin in aortic tissue

Zusammenfassung Es wird eine vereinfachte Methode zur quantitativen Bestimmung des Heparins, bei Ausschliessung anderer Mucopolysaccharide, sowie Hyaluronsäure, Chondroitinschwefelsäure und Heparitinschwefelsäure, beschrieben. Es ergibt sich, dass das Aortagewebe des Rindes in kleinen Mengen Heparin enthält, während es in der menschlichen Aorta nicht gefunden werden konnte.

---

---

On leave from University of Tokyo School of Medicine, Tokyo (Japan).     

Self-attenuation and geometry in single-crystal whole-body spectrometry. application to total potassium measurement in man

Cellular and Molecular Life Sciences - Les avantages d'un spectromètre à chambre d'acier avec grand monocristal de NaI (Tl) sont tout d'abord comparés à ceux...     

Mortality forecasting using neural networks and an application to cause‐specific data for insurance purposes

Mortality forecasting is important for life insurance policies, as well as in other areas. Current techniques for forecasting mortality in the USA involve the use of the Lee–Carter model, which is primarily used without regard to cause. A method for forecasting morality is proposed which involves the use of neural networks. A comparative analysis is done between the Lee–Carter model, linear trend and the proposed method. The results confirm that the use of neural networks performs better than the Lee–Carter and linear trend model within 5% error. Furthermore, mortality rates and life expectancy were formulated for individuals with a specific cause based on prevalence data. The rates are broken down further into respective stages (cancer) based on the individual's diagnosis. Therefore, this approach allows life expectancy to be calculated based on an individual's state of health. Copyright © 2008 John Wiley & Sons, Ltd.     

A model of calibration for subjective probability distributions and its application to aggregation of judgements

A simple model is proposed of the statistical structure underlying the calibration of auditors' subjective probability distributions for account balances, and potentially for other unknown quantities. The model relates calibration curve shape to two parameters which represent over- or underconfidence and over- or underestimation. It is fitted to data from expert auditors. Different types of account appear to have different calibration characteristics. The model helps predict approximately the effects on calibration of aggregating individual subject distributions. Aggregation improves accuracy, but produces a strong tendency towards underconfidence. One aggregation method, predicting the best judgement in the group and using it as the group judgement, is found to be quite effective, much better than averaging the fractiles of individual distributions.     

A method for freezing living ovaries ofDrosophila melanogaster larvae and its application to the storage of mutant stocks

Zusammenfassung Eine Methode zum Gefrieren lebender Ovarien vonDrosophila melanogaster-Larven wird beschrieben Diese Gefrierkonservierung ist geeignet, Mutantenstämme aufzubewahren

---

---

The support of this work by the National Science Foundation (grant No. GB-17267X2) and the American-Swiss Foundation for Scientific Exchange is gratefully acknowledged.     

The micro-focal X-ray unit and its application to bio-medical research

Summary The micro-focal X-ray unit is a modified Cosslett and Nixon X-ray microscope of greater operational stability and flexibility. Its combination with a closed circuit television system provides a quick method of obtaining a point source of X-rays to examine the detailed structure of organs and biological specimens.Acknowledgments. This work has only been possible through the kindness and personal generosity of R. V. Ely. I wish to thank Mr J. Ralph and Mr D. Haig of Eltron International for their help with the C. C. T. V. system and Mr K. Fitzpatrick for preparing the photographs.     

Methods for backcasting,nowcasting and forecasting using factor‐MIDAS: With an application to Korean GDP

We utilize mixed‐frequency factor‐MIDAS models for the purpose of carrying out backcasting, nowcasting, and forecasting experiments using real‐time data. We also introduce a new real‐time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor‐MIDAS prediction models. Our key empirical findings as follows. (i) When using real‐time data, factor‐MIDAS prediction models outperform various linear benchmark models. Interestingly, the “MSFE‐best” MIDAS models contain no autoregressive (AR) lag terms when backcasting and nowcasting. AR terms only begin to play a role in “true” forecasting contexts. (ii) Models that utilize only one or two factors are “MSFE‐best” at all forecasting horizons, but not at any backcasting and nowcasting horizons. In these latter contexts, much more heavily parametrized models with many factors are preferred. (iii) Real‐time data are crucial for forecasting Korean gross domestic product, and the use of “first available” versus “most recent” data “strongly” affects model selection and performance. (iv) Recursively estimated models are almost always “MSFE‐best,” and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our “MSFE‐best” factor‐MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.     

Molecular recognition in antibodies and its application

The structure and function of immunoglobulins, and the nature of the antibody — antigen interaction are described. Applications of the molecular recognition properties of antibodies are discussed in the areas of immunotherapy, immunoassay, immunotargeting and catalytic antibodies.     

A state space approach to forecasting the final vintage of revised data with an application to the index of industrial production

There is considerable interest in the index of industrial production (IIP) as an indicator of the state of the UK's industrial base and, more generally, as a leading economic indicator. However, this index, in common with a number of key macroeconomic time series, is subject to revision as more information becomes available. This raises the problem of forecasting the final vintage of data on IIP. We construct a state space model to solve this problem which incorporates bias adjustments, a model of the measurement error process, and a dynamic model for the final vintage of IIP. Application of the Kalman filter produces an optimal forecast of the final vintage of data.