Anwendung des EM-Algorithmus zur Schätzung der Parameter von Fluoreszenzlebensdauerfunktionen

Die Bachelorarbeit behandelt die Schätzung der Parameter von Fluoreszenzlebensdauerfunktionen mit Hilfe des EM-Algorithmus. Dabei wird der Algorithmus sowohl auf simulierte als auch auf gemessene Daten angewandt. Die Schätzung der Parameter erfolgt zunächst global für die gesamte Probe mit Hilfe eines Simplex-Verfahrens, um dann das Verhältnis der Komponenten der Fluoreszenzlebensdauer, also die Wahrscheinlichkeit, mit der ein Photon von einer Komponente stammt, für jedes Pixel eines Bildes durch den EM-Algorithmus zu bestimmen. Die Messungen liegen als Anzahl der gemessenen Photonen in diskreten Zeitintervallen vor, dabei fehlt jedoch die Information, wie viele der Photonen in einem der Intervalle zu einer Komponente gehören. Durch die Nutzung bedingter Erwartungswerte ist der EM-Algorithmus in der Lage, ohne Verzerrung mit diesen unbekannten Daten umzugehen. Weiterhin wird die Schätzung dadurch erschwert, dass die Daten durch Faltung der Fluoreszenzlebensdauerfunktion mit einer so genannten Apparatefunktion zustandekommen und das Modell somit sehr komplex wird. Auch für dieses Problem wird im Laufe der Arbeit eine Lösung vorgestellt.

Optimal experimental design for parameter identification and model selection

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2014

Untersuchungen zur Perzeption musikalischer Parameter von Cochlea-Implantat-Trägern mit den Sprachkodierungsstrategien ACE und MP3000 im Vergleich zu normalhörenden Probanden

Magdeburg, Univ., Med. Fak., Diss., 2014

Aging and growth parameter from the Piaractus mesopotamicus (pacu) from the Cuiabá river, Mato Grosso, Brazil

This study has aims to determine the age and to estimate the growth parameters using scales of the species. Individuals of Piaractus mesopotamicus (Holmberg, 1887) used in this study were captured in the commercial fishery conducted in the region, along the year 2006. The model selected to express the growth of the species was the von Bertalanffy Sl= Sl∞*[1-exp-k(t-to)]. To determine if scales are suitable for studying the growth of pacu, we analyzed the relation between standard length (Sl) and the radius of the scales through linear regression. The period of annuli formation was determined analyzing the variations in the marginal increment and evaluating the consistency of the readings through the analysis of the coefficient of variations (CVs) for the average standard lengths of each age (number of rings) observed in the scales. The relationship between Ls of the fish and the radius of the scales showed that scales can be used to study the age and growth of P. mesopotamicus (R= 0.79). CVs were always below 20%, demonstrating the consistency of the readings. Annuli formation occurred in February, probably related to trophic migration that occurs in this month in the region. Equations that represents the growth in length obtained for P. mesopotamicus are Sl=50.00*[1-exp-0.18(t-(-3.00)] for males and Sl=59.23*[1-exp-0.14(t-(-3.36)] for females. The growth parameters obtained in this study were lower compared to other studies previously conducted for the same species and can related to overexploitation that species is submitted by fishing in the region. These values show also that females of pacu attain greater asymptotic length than males that growth faster.

Large Time-Varying Parameter VARs

In this paper we develop methods for estimation and forecasting in large timevarying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output, and interest rates demonstrates the feasibility and usefulness of our approach.

Model Switching and Model Averaging in Time- Varying Parameter Regression Models

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.

Hierarchical Shrinkage in Time-Varying Parameter Models

In this paper, we forecast EU-area inflation with many predictors using time-varying parameter models. The facts that time-varying parameter models are parameter-rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. In this paper, we develop econometric methods for using the Bayesian Lasso with time-varying parameter models. Our approach allows for the coefficient on each predictor to be: i) time varying, ii) constant over time or iii) shrunk to zero. The econometric methodology decides automatically which category each coefficient belongs in. Our empirical results indicate the benefits of such an approach.

A parameter-free approach for solving combinatorial optimization problems through biased randomization of efficient heuristics

This paper discusses the use of probabilistic or randomized algorithms for solving combinatorial optimization problems. Our approach employs non-uniform probability distributions to add a biased random behavior to classical heuristics so a large set of alternative good solutions can be quickly obtained in a natural way and without complex conguration processes. This procedure is especially useful in problems where properties such as non-smoothness or non-convexity lead to a highly irregular solution space, for which the traditional optimization methods, both of exact and approximate nature, may fail to reach their full potential. The results obtained are promising enough to suggest that randomizing classical heuristics is a powerful method that can be successfully applied in a variety of cases.

Meaning of the λ parameter of skew–normal and log–skew normal distributions in fluid geochemistry

The literature related to skew–normal distributions has grown rapidly in recent yearsbut at the moment few applications concern the description of natural phenomena withthis type of probability models, as well as the interpretation of their parameters. Theskew–normal distributions family represents an extension of the normal family to whicha parameter (λ) has been added to regulate the skewness. The development of this theoreticalfield has followed the general tendency in Statistics towards more flexible methodsto represent features of the data, as adequately as possible, and to reduce unrealisticassumptions as the normality that underlies most methods of univariate and multivariateanalysis. In this paper an investigation on the shape of the frequency distribution of thelogratio ln(Cl−/Na+) whose components are related to waters composition for 26 wells,has been performed. Samples have been collected around the active center of Vulcanoisland (Aeolian archipelago, southern Italy) from 1977 up to now at time intervals ofabout six months. Data of the logratio have been tentatively modeled by evaluating theperformance of the skew–normal model for each well. Values of the λ parameter havebeen compared by considering temperature and spatial position of the sampling points.Preliminary results indicate that changes in λ values can be related to the nature ofenvironmental processes affecting the data

Total hemoglobin mass--a new parameter to detect blood doping?

PURPOSE: All kinds of blood manipulations aim to increase the total hemoglobin mass (tHb-mass). To establish tHb-mass as an effective screening parameter for detecting blood doping, the knowledge of its normal variation over time is necessary. The aim of the present study, therefore, was to determine the intraindividual variance of tHb-mass in elite athletes during a training year emphasizing off, training, and race seasons at sea level. METHODS: tHb-mass and hemoglobin concentration ([Hb]) were determined in 24 endurance athletes five times during a year and were compared with a control group (n = 6). An analysis of covariance was used to test the effects of training phases, age, gender, competition level, body mass, and training volume. Three error models, based on 1) a total percentage error of measurement, 2) the combination of a typical percentage error (TE) of analytical origin with an absolute SD of biological origin, and 3) between-subject and within-subject variance components as obtained by an analysis of variance, were tested. RESULTS: In addition to the expected influence of performance status, the main results were that the effects of training volume (P = 0.20) and training phases (P = 0.81) on tHb-mass were not significant. We found that within-subject variations mainly have an analytical origin (TE approximately 1.4%) and a very small SD (7.5 g) of biological origin. CONCLUSION: tHb-mass shows very low individual oscillations during a training year (<6%), and these oscillations are below the expected changes in tHb-mass due to Herythropoetin (EPO) application or blood infusion (approximately 10%). The high stability of tHb-mass over a period of 1 year suggests that it should be included in an athlete's biological passport and analyzed by recently developed probabilistic inference techniques that define subject-based reference ranges.

Trabecular bone score (TBS) the new parameter of 2D texture analysis for the evaluation of 3D bone micro architecture status

X-ray is a technology that is used for numerous applications in the medical field. The process of X-ray projection gives a 2-dimension (2D) grey-level texture from a 3- dimension (3D) object. Until now no clear demonstration or correlation has positioned the 2D texture analysis as a valid indirect evaluation of the 3D microarchitecture. TBS is a new texture parameter based on the measure of the experimental variogram. TBS evaluates the variation between 2D image grey-levels. The aim of this study was to evaluate existing correlations between 3D bone microarchitecture parameters - evaluated from μCT reconstructions - and the TBS value, calculated on 2D projected images. 30 dried human cadaveric vertebrae were acquired on a micro-scanner (eXplorer Locus, GE) at isotropic resolution of 93 μm. 3D vertebral body models were used. The following 3D microarchitecture parameters were used: Bone volume fraction (BV/TV), Trabecular thickness (TbTh), trabecular space (TbSp), trabecular number (TbN) and connectivity density (ConnD). 3D/2D projections has been done by taking into account the Beer-Lambert Law at X-ray energy of 50, 100, 150 KeV. TBS was assessed on 2D projected images. Correlations between TBS and the 3D microarchitecture parameters were evaluated using a linear regression analysis. Paired T-test is used to assess the X-ray energy effects on TBS. Multiple linear regressions (backward) were used to evaluate relationships between TBS and 3D microarchitecture parameters using a bootstrap process. BV/TV of the sample ranged from 18.5 to 37.6% with an average value at 28.8%. Correlations' analysis showedthat TBSwere strongly correlatedwith ConnD(0.856≤r≤0.862; p<0.001),with TbN (0.805≤r≤0.810; p<0.001) and negatively with TbSp (−0.714≤r≤−0.726; p<0.001), regardless X-ray energy. Results show that lower TBS values are related to "degraded" microarchitecture, with low ConnD, low TbN and a high TbSp. The opposite is also true. X-ray energy has no effect onTBS neither on the correlations betweenTBS and the 3Dmicroarchitecture parameters. In this study, we demonstrated that TBS was significantly correlated with 3D microarchitecture parameters ConnD and TbN, and negatively with TbSp, no matter what X-ray energy has been used. This article is part of a Special Issue entitled ECTS 2011. Disclosure of interest: None declared.

Intraocular Straylight And Contrast Sensitivity Two Months After Lasik: Intraocular Straylight Measurement As A New Parameter In Visual Quality Assessment

PROPÒSIT: Estudiar l'efecte de la cirurgia LASIK en la llum dispersa i la sensibilitat al contrast. MÈTODES: Vint-i-vuit pacients van ser tractats amb LASIK. La qualitat visual es va avaluar abans de l'operació i dos mesos després. RESULTATS: La mitjana de llum dispersa i la sensibilitat al contrast abans de l'operació no va canviar en dos mesos després. Només un ull tenia un marcat augment en la llum dispersa. Nou ulls van presentar una lleugera disminució en la sensibilitat al contrast. S'han trobat dues complicacions. CONCLUSIÓ: Després de LASIK la majoria dels pacients (80%) no van tenir complicacions i van mantenir la seva qualitat visual. Uns pocs pacients (16%) van tenir una mica de qualitat visual disminuïda. Molt pocs (4%) van tenir complicacions clíniques amb disminució en la qualitat visual.

Fault diagnosis by refining the parameter uncertainty space of nonlinear dynamic systems

This paper deals with fault detection and isolation problems for nonlinear dynamic systems. Both problems are stated as constraint satisfaction problems (CSP) and solved using consistency techniques. The main contribution is the isolation method based on consistency techniques and uncertainty space refining of interval parameters. The major advantage of this method is that the isolation speed is fast even taking into account uncertainty in parameters, measurements, and model errors. Interval calculations bring independence from the assumption of monotony considered by several approaches for fault isolation which are based on observers. An application to a well known alcoholic fermentation process model is presented

A hierarchical approach to model parameter optimization for developmental systems.

In the context of Systems Biology, computer simulations of gene regulatory networks provide a powerful tool to validate hypotheses and to explore possible system behaviors. Nevertheless, modeling a system poses some challenges of its own: especially the step of model calibration is often difficult due to insufficient data. For example when considering developmental systems, mostly qualitative data describing the developmental trajectory is available while common calibration techniques rely on high-resolution quantitative data. Focusing on the calibration of differential equation models for developmental systems, this study investigates different approaches to utilize the available data to overcome these difficulties. More specifically, the fact that developmental processes are hierarchically organized is exploited to increase convergence rates of the calibration process as well as to save computation time. Using a gene regulatory network model for stem cell homeostasis in Arabidopsis thaliana the performance of the different investigated approaches is evaluated, documenting considerable gains provided by the proposed hierarchical approach.