Robust response transformations based on optimal prediction

Nonlinear regression problems can often be reduced to linearity by transforming the response variable (e.g., using the Box-Cox family of transformations). The classic estimates of the parameter defining the transformation as well as of the regression coefficients are based on the maximum likelihood criterion, assuming homoscedastic normal errors for the transformed response. These estimates are nonrobust in the presence of outliers and can be inconsistent when the errors are nonnormal or heteroscedastic. This article proposes new robust estimates that are consistent and asymptotically normal for any unimodal and homoscedastic error distribution. For this purpose, a robust version of conditional expectation is introduced for which the prediction mean squared error is replaced with an M scale. This concept is then used to develop a nonparametric criterion to estimate the transformation parameter as well as the regression coefficients. A finite sample estimate of this criterion based on a robust version of smearing is also proposed. Monte Carlo experiments show that the new estimates compare favorably with respect to the available competitors.

Transformed symmetric models

For the first time, we introduce a class of transformed symmetric models to extend the Box and Cox models to more general symmetric models. The new class of models includes all symmetric continuous distributions with a possible non-linear structure for the mean and enables the fitting of a wide range of models to several data types. The proposed methods offer more flexible alternatives to Box-Cox or other existing procedures. We derive a very simple iterative process for fitting these models by maximum likelihood, whereas a direct unconditional maximization would be more difficult. We give simple formulae to estimate the parameter that indexes the transformation of the response variable and the moments of the original dependent variable which generalize previous published results. We discuss inference on the model parameters. The usefulness of the new class of models is illustrated in one application to a real dataset.

Bayesian model selection methodology for road safety

Road accidents are a very relevant issue in many countries and macroeconomic models are very frequently applied by academia and administrations to reduce their frequency and consequences. The selection of explanatory variables and response transformation parameter within the Bayesian framework for the selection of the set of explanatory variables a TIM and 3IM (two input and three input models) procedures are proposed. The procedure also uses the DIC and pseudo -R2 goodness of fit criteria. The model to which the methodology is applied is a dynamic regression model with Box-Cox transformation (BCT) for the explanatory variables and autorgressive (AR) structure for the response. The initial set of 22 explanatory variables are identified. The effects of these factors on the fatal accident frequency in Spain, during 2000-2012, are estimated. The dependent variable is constructed considering the stochastic trend component.

A swarm pattern transformation method based on macroscopic parameter operations

The work reported in this paper is motivated by biomimetic inspiration - the transformation of patterns. The major issue addressed is the development of feasible methods for transformation based on a macroscopic tool. The general requirement for the feasibility of the transformation method is determined by classifying pattern formation approaches an their characteristics. A formal definition for pattern transformation is provided and four special cases namely, elementary and geometric transformation based on repositioning all and some robotic agents are introduced. A feasible method for transforming patterns geometrically, based on the macroscopic parameter operation of a swarm is considered. The transformation method is applied to a swarm model which lends itself to the transformation technique. Simulation studies are developed to validate the feasibility of the approach, and do indeed confirm the approach.

Transformation kernel density estimation of actuarial loss functions

[cat] Es presenta un estimador nucli transformat que és adequat per a distribucions de cua pesada. Utilitzant una transformació basada en la distribució de probabilitat Beta l’elecció del paràmetre de finestra és molt directa. Es presenta una aplicació a dades d’assegurances i es mostra com calcular el Valor en Risc.

Transformation kernel density estimation of actuarial loss functions

[cat] Es presenta un estimador nucli transformat que és adequat per a distribucions de cua pesada. Utilitzant una transformació basada en la distribució de probabilitat Beta l’elecció del paràmetre de finestra és molt directa. Es presenta una aplicació a dades d’assegurances i es mostra com calcular el Valor en Risc.

Foam pore size is a critical interface parameter of suction-based wound healing devices.

BACKGROUND: Suction-based wound healing devices with open-pore foam interfaces are widely used to treat complex tissue defects. The impact of changes in physicochemical parameters of the wound interfaces has not been investigated. METHODS: Full-thickness wounds in diabetic mice were treated with occlusive dressing or a suction device with a polyurethane foam interface varying in mean pore size diameter. Wound surface deformation on day 2 was measured on fixed tissues. Histologic cross-sections were analyzed for granulation tissue thickness (hematoxylin and eosin), myofibroblast density (α-smooth muscle actin), blood vessel density (platelet endothelial cell adhesion molecule-1), and cell proliferation (Ki67) on day 7. RESULTS: Polyurethane foam-induced wound surface deformation increased with polyurethane foam pore diameter: 15 percent (small pore size), 60 percent (medium pore size), and 150 percent (large pore size). The extent of wound strain correlated with granulation tissue thickness that increased 1.7-fold in small pore size foam-treated wounds, 2.5-fold in medium pore size foam-treated wounds, and 4.9-fold in large pore size foam-treated wounds (p < 0.05) compared with wounds treated with an occlusive dressing. All polyurethane foams increased the number of myofibroblasts over occlusive dressing, with maximal presence in large pore size foam-treated wounds compared with all other groups (p < 0.05). CONCLUSIONS: The pore size of the interface material of suction devices has a significant impact on the wound healing response. Larger pores increased wound surface strain, tissue growth, and transformation of contractile cells. Modification of the pore size is a powerful approach for meeting biological needs of specific wounds.

Modeling and Parameter Estimation of Double-Star Permanent Magnet Synchronous Machines

The power rating of wind turbines is constantly increasing; however, keeping the voltage rating at the low-voltage level results in high kilo-ampere currents. An alternative for increasing the power levels without raising the voltage level is provided by multiphase machines. Multiphase machines are used for instance in ship propulsion systems, aerospace applications, electric vehicles, and in other high-power applications including wind energy conversion systems. A machine model in an appropriate reference frame is required in order to design an efficient control for the electric drive. Modeling of multiphase machines poses a challenge because of the mutual couplings between the phases. Mutual couplings degrade the drive performance unless they are properly considered. In certain multiphase machines there is also a problem of high current harmonics, which are easily generated because of the small current path impedance of the harmonic components. However, multiphase machines provide special characteristics compared with the three-phase counterparts: Multiphase machines have a better fault tolerance, and are thus more robust. In addition, the controlled power can be divided among more inverter legs by increasing the number of phases. Moreover, the torque pulsation can be decreased and the harmonic frequency of the torque ripple increased by an appropriate multiphase configuration. By increasing the number of phases it is also possible to obtain more torque per RMS ampere for the same volume, and thus, increase the power density. In this doctoral thesis, a decoupled d–q model of double-star permanent-magnet (PM) synchronous machines is derived based on the inductance matrix diagonalization. The double-star machine is a special type of multiphase machines. Its armature consists of two three-phase winding sets, which are commonly displaced by 30 electrical degrees. In this study, the displacement angle between the sets is considered a parameter. The diagonalization of the inductance matrix results in a simplified model structure, in which the mutual couplings between the reference frames are eliminated. Moreover, the current harmonics are mapped into a reference frame, in which they can be easily controlled. The work also presents methods to determine the machine inductances by a finite-element analysis and by voltage-source inverters on-site. The derived model is validated by experimental results obtained with an example double-star interior PM (IPM) synchronous machine having the sets displaced by 30 electrical degrees. The derived transformation, and consequently, the decoupled d–q machine model, are shown to model the behavior of an actual machine with an acceptable accuracy. Thus, the proposed model is suitable to be used for the model-based control design of electric drives consisting of double-star IPM synchronous machines.

Unusual surface and edge morphologies, sp2 to sp3 hybridized transformation and electronic damage after Ar+ ion irradiation of few-layer graphene surfaces

Roughness and defects induced on few-layer graphene (FLG) irradiated by Ar+ ions at different energies were investigated using X-ray photoemission spectroscopy (XPS) and atomic force microscopy techniques. The results provide direct experimental evidence of ripple formation, sp2 to sp3 hybridized carbon transformation, electronic damage, Ar+ implantation, unusual defects and edge reconstructions in FLG, which depend on the irradiation energy. In addition, shadowing effects similar to those found in oblique-angle growth of thin films were seen. Reliable quantification of the transition from the sp2-bonding to sp3-hybridized state as a result of Ar+ ion irradiation is achieved from the deconvolution of the XPS C (1s) peak. Although the ion irradiation effect is demonstrated through the shape of the derivative of the Auger transition C KVV spectra, we show that the D parameter values obtained from these spectra which are normally used in the literature fail to account for the sp2 to sp3 hybridization transition. In contrast to what is known, it is revealed that using ion irradiation at large FLG sample tilt angles can lead to edge reconstructions. Furthermore, FLG irradiation by low energy of 0.25 keV can be a plausible way of peeling graphene layers without the need of Joule heating reported previously

Towards a unifying framework for pattern transformation in swarm systems

This paper identifies the major challenges in the area of pattern formation. The work is also motivated by the need for development of a single framework to surmount these challenges. A framework based on the control of macroscopic parameters is proposed. The issue of transformation of patterns is specifically considered. A definition for transformation and four special cases, namely elementary and geometrical transformations by repositioning all or some robots in the pattern are provided. Two feasible tools for pattern transformation namely, a macroscopic parameter method and a mathematical tool - Moebius transformation also known as the linear fractional transformation are introduced. The realization of the unifying framework considering planning and communication is reported.

Non-transposed three-phase line analyses with a single real transformation matrix

In transmission line transient analyses, a single real transformation matrix can obtain exact modes when the analyzed line is transposed. For non-transposed lines, the results are not exact. In this paper, non-symmetrical and non transposed three-phase line samples are analyzed with a single real transformation matrix application (Clarke's matrix). Some interesting characteristics of this matrix application are: single, real, frequency independent, line parameter independent, identical for voltage and current determination. With Clarke's matrix use, mathematical simplifications are obtained and the developed model can be applied directly in programs based on time domain. This model works without convolution procedures to deal with phase-mode transformation. In EMTP programs, Clarke's matrix can be represented by ideal transformers and the frequency dependent line parameters can be represented by modified-circuits. With these representations, the electrical values at any line point can be accessed for phase domain or mode domain using the Clarke matrix or its inverse matrix. For symmetrical and non-transposed lines, the model originates quite small errors. In addition, the application of the proposed model to the non-symmetrical and non-transposed three phase transmission lines is investigated. ©2005 IEEE.

Untersuchung zur Bestimmung quantitativer Parameter der Lungenventilation mittels CT und MRT

Die zuverlässige Berechnung von quantitativen Parametern der Lungenventilation ist für ein Verständnis des Verhaltens der Lunge und insbesondere für die Diagnostik von Lungenerkrankungen von großer Bedeutung. Nur durch quantitative Parameter sind verlässliche und reproduzierbare diagnostische Aussagen über den Gesundheitszustand der Lunge möglich. Im Rahmen dieser Arbeit wurden neue quantitative Verfahren zur Erfassung der Lungenventilation basierend auf der dynamischen Computer- (CT) und Magnetresonanztomographie (MRT) entwickelt. Im ersten Teil dieser Arbeit wurde die Frage untersucht, ob das Aufblähen der Lunge in gesunden Schweinelungen und Lungen mit Akutem Lungenversagen (ARDS) durch einzelne, diskrete Zeitkonstanten beschrieben werden kann, oder ob kontinuierliche Verteilungen von Zeitkonstanten die Realität besser beschreiben. Hierzu wurden Serien dynamischer CT-Aufnahmen während definierter Beatmungsmanöver (Drucksprünge) aufgenommen und anschließend aus den Messdaten mittels inverser Laplace-Transformation die zugehörigen Verteilungen der Zeitkonstanten berechnet. Um die Qualität der Ergebnisse zu analysieren, wurde der Algorithmus im Rahmen von Simulationsrechnungen systematisch untersucht und anschließend in-vivo an gesunden und ARDS-Schweinelungen eingesetzt. Während in den gesunden Lungen mono- und biexponentielle Verteilungen bestimmt wurden, waren in den ARDS-Lungen Verteilungen um zwei dominante Zeitkonstanten notwendig, um die gemessenen Daten auf der Basis des verwendeten Modells verlässlich zu beschreiben. Es wurden sowohl diskrete als auch kontinuierliche Verteilungen gefunden. Die CT liefert Informationen über das solide Lungengewebe, während die MRT von hyperpolarisiertem 3He in der Lage ist, direkt das eingeatmete Gas abzubilden. Im zweiten Teil der Arbeit wurde zeitlich hochaufgelöst das Einströmen eines 3He-Bolus in die Lunge erfasst. Über eine Entfaltungsanalyse wurde anschließend das Einströmverhalten unter Idealbedingungen (unendlich kurzer 3He-Bolus), also die Gewebeantwortfunktion, berechnet und so eine Messtechnik-unabhängige Erfassung des Einströmens von 3He in die Lunge ermöglicht. Zentrale Fragestellung war hier, wie schnell das Gas in die Lunge einströmt. Im Rahmen von Simulationsrechnungen wurde das Verhalten eines Entfaltungsalgorithmus (basierend auf B-Spline Repräsentationen) systematisch analysiert. Zusätzlich wurde ein iteratives Entfaltungsverfahren eingesetzt. Aus zeitlich hochaufgelösten Messungen (7ms) an einer gesunden und einer ARDS-Schweinelunge konnte erstmals nachgewiesen werden, dass das Einströmen in-vivo in weniger als 0,1s geschieht. Die Ergebnisse zeigen Zeitkonstanten im Bereich von 4ms–50ms, wobei zwischen der gesunden Lungen und der ARDS-Lunge deutliche Unterschiede beobachtet wurden. Zusammenfassend ermöglichen daher die in dieser Arbeit vorgestellten Algorithmen eine objektivere Bestimmung quantitativer Parameter der Lungenventilation. Dies ist für die eindeutige Beschreibung ventilatorischer Vorgänge in der Lunge und somit für die Lungendiagnostik unerlässlich. Damit stehen quantitative Methoden für die Lungenfunktionsdiagnostik zur Verfügung, deren diagnostische Relevanz im Rahmen wissenschaftlicher und klinischer Studien untersucht werden kann.

Semiparametric Normal Transformation Models for Spatially Correlated Survival Data

There is an emerging interest in modeling spatially correlated survival data in biomedical and epidemiological studies. In this paper, we propose a new class of semiparametric normal transformation models for right censored spatially correlated survival data. This class of models assumes that survival outcomes marginally follow a Cox proportional hazard model with unspecified baseline hazard, and their joint distribution is obtained by transforming survival outcomes to normal random variables, whose joint distribution is assumed to be multivariate normal with a spatial correlation structure. A key feature of the class of semiparametric normal transformation models is that it provides a rich class of spatial survival models where regression coefficients have population average interpretation and the spatial dependence of survival times is conveniently modeled using the transformed variables by flexible normal random fields. We study the relationship of the spatial correlation structure of the transformed normal variables and the dependence measures of the original survival times. Direct nonparametric maximum likelihood estimation in such models is practically prohibited due to the high dimensional intractable integration of the likelihood function and the infinite dimensional nuisance baseline hazard parameter. We hence develop a class of spatial semiparametric estimating equations, which conveniently estimate the population-level regression coefficients and the dependence parameters simultaneously. We study the asymptotic properties of the proposed estimators, and show that they are consistent and asymptotically normal. The proposed method is illustrated with an analysis of data from the East Boston Ashma Study and its performance is evaluated using simulations.

Lymphocyte transformation by insulin and insulin-zinc suspensions

The lymphocyte transformation response to the mitogen phytohaemagglutinin (PHA) was determined in 15 well controlled insulin-dependent diabetics (IDD) with a history of insulin allergy or an acute insulin allergy. There was no significant difference in the PHA response of IDD and normal subjects matched in respect of age and sex. The response of peripheral blood lymphocytes to insulin (Actrapid) and an insulin zinc suspension (Monotard) was also determined. Fifty-three percent of IDD gave a positive reaction to Actrapid. Monotard produced positive reactions both in IDD and normal subjects. In normal subjects, a close correlation between the stimulation indices of Monotard and PHA was found (r = 0 . 966) suggesting that these stimulations depend on a common parameter namely, the reactivity to mitogens.

Sources, Fate and Transformation of Organic Matter in Wetlands and Estuaries

Dissolved organic matter (DOM) is a complex mixture of organic compounds and represents the largest reservoirs of carbon (C) on earth. Particulate organic matter (POM) is another important carbon component in C cycling and controls a variety of biogeochemical processes. Estuaries, as important interfaces between land and ocean, play important roles in retaining and transforming such organic matter (OM) and serve as both sources and sinks of DOM and POM. There is a diverse array of both autochthonous and allochthonous OM sources in wetland/estuarine ecosystems. A comprehensive study on the sources, transformation and fate of OM in such ecosystems is essential in advancing our understanding of C cycling and better constraining the global C budget. In this work, DOM characteristics were investigated in different estuaries. Dissolved organic matter source strengths and dynamics were assessed in a seagrass-dominated subtropical estuarine lagoon. DOM dynamics controlled by hydrology and seagrass primary productivity were confirmed, and the primary source of DOM was quantified using the combination of excitation emission matrix fluorescence with parallel factor analysis (EEM-PARAFAC) and stable C isotope analysis. Seagrass can contribute up to 72% of the DOM in the study area. The spatial and temporal variation of DOM dynamics was also studied in a freshwated dominated estuary fringed with extensive salt marshes. The data showed that DOM was primarily derived from freshwater marshes and controlled by hydrology while salt marsh plants play a significant role in structuring the distribution patterns of DOM quality and quantity. The OM dynamics was also investigated in a mangrove-dominate estuary and a comparative study was conducted between the DOM and POM pools. The results revealed both similarity and dissimilarity in DOM and POM composition. The dynamics of both OM pools are largely uncoupled as a result of source differences. Fringe mangrove swamps are suggested to export similar amounts of DOM and POM and should be considered as an important source in coastal C budgets. Lastly, chemical characterizations were conducted on the featured fluorescence component in OM in an attempt to better understand the composition and origins of the specific PARAFAC component. The traditionally defined ‘protein-like’ fluorescence was found to contain both proteinaceous and phenolic compounds, suggesting that the application of this parameter as a proxy for amino acid content and bioavailability may be limited.