An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks Data

We consider a mixture model approach to the regression analysis of competing-risks data. Attention is focused on inference concerning the effects of factors on both the probability of occurrence and the hazard rate conditional on each of the failure types. These two quantities are specified in the mixture model using the logistic model and the proportional hazards model, respectively. We propose a semi-parametric mixture method to estimate the logistic and regression coefficients jointly, whereby the component-baseline hazard functions are completely unspecified. Estimation is based on maximum likelihood on the basis of the full likelihood, implemented via an expectation-conditional maximization (ECM) algorithm. Simulation studies are performed to compare the performance of the proposed semi-parametric method with a fully parametric mixture approach. The results show that when the component-baseline hazard is monotonic increasing, the semi-parametric and fully parametric mixture approaches are comparable for mildly and moderately censored samples. When the component-baseline hazard is not monotonic increasing, the semi-parametric method consistently provides less biased estimates than a fully parametric approach and is comparable in efficiency in the estimation of the parameters for all levels of censoring. The methods are illustrated using a real data set of prostate cancer patients treated with different dosages of the drug diethylstilbestrol. Copyright (C) 2003 John Wiley Sons, Ltd.

Semi-parametric geolocation estimation in NLOS environments

The position of a stationary target can be determined using triangulation in combination with time of arrival measurements at several sensors. In urban environments, none-line-of-sight (NLOS) propagation leads to biased time estimation and thus to inaccurate position estimates. Here, a semi-parametric approach is proposed to mitigate the effects of NLOS propagation. The degree of contamination by NLOS components in the observations, which result in asymmetric noise statistics, is determined and incorporated into the estimator. The proposed method is adequate for environments where the NLOS error plays a dominant role and outperforms previous approaches that assume a symmetric noise statistic.

The reasons behind the progression in PISA scores: An education production function approach using semi-parametric techniques

Research Masters

A (semi-)parametric functional coefficient autoregressive conditional duration model

In this paper, we propose a class of ACD-type models that accommodates overdispersion, intermittent dynamics, multiple regimes, and sign and size asymmetries in financial durations. In particular, our functional coefficient autoregressive conditional duration (FC-ACD) model relies on a smooth-transition autoregressive specification. The motivation lies on the fact that the latter yields a universal approximation if one lets the number of regimes grows without bound. After establishing that the sufficient conditions for strict stationarity do not exclude explosive regimes, we address model identifiability as well as the existence, consistency, and asymptotic normality of the quasi-maximum likelihood (QML) estimator for the FC-ACD model with a fixed number of regimes. In addition, we also discuss how to consistently estimate using a sieve approach a semiparametric variant of the FC-ACD model that takes the number of regimes to infinity. An empirical illustration indicates that our functional coefficient model is flexible enough to model IBM price durations.

A semi-empirical approach to optimise the quantity of drying aids required to spray dry sugar-rich foods

A semi-empirical linear equation has been developed to optimise the amount of maltodextrin additive (DE 6) required to successfully spray dry a sugar-rich product on the basis of its composition. Based on spray drying experiments, drying index values for individual sugars (sucrose, glucose, frutose) and citric acid were determined, and us;ng these index values an equation for model mixtures of these components was established. This equation has been tested with two sugar-rich natural products, pineapple juice and honey. The relationship was found to be valid for these products.

Technical, allocative, cost and scale efficiencies in Bangladesh rice cultivation: A non-parametric approach

Applying programming techniques to detailed data for 406 rice farms in 21 villages, for 1997, produces inefficiency measures, which differ substantially from the results of simple yield and unit cost measures. For the Boro (dry) season, mean technical efficiency was efficiency was 56.2 per cent and 69.4 per cent, allocative efficiency was 81.3 per cent, cost efficiency was 56.2 per cent and scale efficiency 94.9 per cent. The Aman (wet) season results are similar, but a few points lower. Allocative inefficiency is due to overuse of labour, suggesting population pressure, and of fertiliser, where recommended rates may warrant revision. Second-stage regressions show that large families are more inefficient, whereas farmers with better access to input markets, and those who do less off-farm work, tend to be more efficient. The information on the sources of inter-farm performance differentials could be used by the extension agents to help inefficient farmers. There is little excuse for such sub-optimal use of survey data, which are often collected at substantial costs.

A semi-automatic approach to code smells detection

Eradication of code smells is often pointed out as a way to improve readability, extensibility and design in existing software. However, code smell detection remains time consuming and error-prone, partly due to the inherent subjectivity of the detection processes presently available. In view of mitigating the subjectivity problem, this dissertation presents a tool that automates a technique for the detection and assessment of code smells in Java source code, developed as an Eclipse plugin. The technique is based upon a Binary Logistic Regression model that uses complexity metrics as independent variables and is calibrated by expert‟s knowledge. An overview of the technique is provided, the tool is described and validated by an example case study.

An Efficient Segmentation Method for Ultrasound Images based on a Semi-supervised Approach and Patch-based Features

Segmenting ultrasound images is a challenging problemwhere standard unsupervised segmentation methods such asthe well-known Chan-Vese method fail. We propose in thispaper an efficient segmentation method for this class ofimages. Our proposed algorithm is based on asemi-supervised approach (user labels) and the use ofimage patches as data features. We also consider thePearson distance between patches, which has been shown tobe robust w.r.t speckle noise present in ultrasoundimages. Our results on phantom and clinical data show avery high similarity agreement with the ground truthprovided by a medical expert.

Parametric Approach to Blind Deconvolution of Nonlinear Channels

A parametric procedure for the blind inversion of nonlinear channels is proposed, based on a recent method of blind source separation in nonlinear mixtures. Experiments show that the proposed algorithms perform efficiently, even in the presence of hard distortion. The method, based on the minimization of the output mutual information, needs the knowledge of log-derivative of input distribution (the so-called score function). Each algorithm consists of three adaptive blocks: one devoted to adaptive estimation of the score function, and two other blocks estimating the inverses of the linear and nonlinear parts of the channel, (quasi-)optimally adapted using the estimated score functions. This paper is mainly concerned by the nonlinear part, for which we propose two parametric models, the first based on a polynomial model and the second on a neural network, while [14, 15] proposed non-parametric approaches.

Pediatric reference intervals for plasma free and total metanephrines established with a parametric approach: Relevance to the diagnosis of neuroblastoma.

BACKGROUND: Urine catecholamines, vanillylmandelic, and homovanillic acid are recognized biomarkers for the diagnosis and follow-up of neuroblastoma. Plasma free (f) and total (t) normetanephrine (NMN), metanephrine (MN) and methoxytyramine (MT) could represent a convenient alternative to those urine markers. The primary objective of this study was to establish pediatric centile charts for plasma metanephrines. Secondarily, we explored their diagnostic performance in 10 patients with neuroblastoma. PROCEDURE: We recruited 191 children (69 females) free of neuroendocrine disease to establish reference intervals for plasma metanephrines, reported as centile curves for a given age and sex based on a parametric method using fractional polynomials models. Urine markers and plasma metanephrines were measured in 10 children with neuroblastoma at diagnosis. Plasma total metanephrines were measured by HPLC with coulometric detection and plasma free metanephrines by tandem LC-MS. RESULTS: We observed a significant age-dependence for tNMN, fNMN, and fMN, and a gender and age-dependence for tMN, fNMN, and fMN. Free MT was below the lower limit of quantification in 94% of the children. All patients with neuroblastoma at diagnosis were above the 97.5th percentile for tMT, tNMN, fNMN, and fMT, whereas their fMN and tMN were mostly within the normal range. As expected, urine assays were inconstantly predictive of the disease. CONCLUSIONS: A continuous model incorporating all data for a given analyte represents an appealing alternative to arbitrary partitioning of reference intervals across age categories. Plasma metanephrines are promising biomarkers for neuroblastoma, and their performances need to be confirmed in a prospective study on a large cohort of patients. Pediatr Blood Cancer 2015;62:587-593. © 2015 Wiley Periodicals, Inc.

Semi-Parametric Weak Instrument Regressions with an Application to the Risk-Return Trade-off

Recent work shows that a low correlation between the instruments and the included variables leads to serious inference problems. We extend the local-to-zero analysis of models with weak instruments to models with estimated instruments and regressors and with higher-order dependence between instruments and disturbances. This makes this framework applicable to linear models with expectation variables that are estimated non-parametrically. Two examples of such models are the risk-return trade-off in finance and the impact of inflation uncertainty on real economic activity. Results show that inference based on Lagrange Multiplier (LM) tests is more robust to weak instruments than Wald-based inference. Using LM confidence intervals leads us to conclude that no statistically significant risk premium is present in returns on the S&P 500 index, excess holding yields between 6-month and 3-month Treasury bills, or in yen-dollar spot returns.

A Semi-Parametric Factor Model for Interest Rates.

Understanding the dynamics of interest rates and the term structure has important implications for issues as diverse as real economic activity, monetary policy, pricing of interest rate derivative securities and public debt financing. Our paper follows a longstanding tradition of using factor models of interest rates but proposes a semi-parametric procedure to model interest rates.

A semi-phenomenological approach to the structure and transport properties of macromolecules in solution

Motiviert durch die Lebenswissenschaften (Life sciences) haben sich Untersuchungen zur Dynamik von Makromolekülen in Lösungen in den vergangenen Jahren zu einem zukunftsweisenden Forschungsgebiet etabliert, dessen Anwendungen von der Biophysik über die physikalische Chemie bis hin zu den Materialwissenschaften reichen. Neben zahlreichen experimentellen Forschungsprogrammen zur räumlichen Struktur und den Transporteigenschaften grosser MolekÄule, wie sie heute praktisch an allen (Synchrotron-) Strahlungsquellen und den Laboren der Biophysik anzutreffen sind, werden gegenwärtig daher auch umfangreiche theoretische Anstrengungen unternommen, um das Diffusionsverhalten von Makromolekülen besser zu erklären. Um neue Wege für eine quantitative Vorhersagen des Translations- und Rotationsverhaltens grosser Moleküle zu erkunden, wurde in dieser Arbeit ein semiphänomenologischer Ansatz verfolgt. Dieser Ansatz erlaubte es, ausgehend von der Hamiltonschen Mechanik des Gesamtsystems 'Molekül + Lösung', eine Mastergleichung für die Phasenraumdichte der Makromoleküle herzuleiten, die den Einfluss der Lösung mittels effektiver Reibungstensoren erfasst. Im Rahmen dieses Ansatzes gelingt es z.B. (i) sowohl den Einfluss der Wechselwirkung zwischen den makromolekularen Gruppen (den sogenannten molekularen beads) und den Lösungsteilchen zu analysieren als auch (ii) die Diffusionseigen schaften für veschiedene thermodynamische Umgebungen zu untersuchen. Ferner gelang es auf der Basis dieser Näherung, die Rotationsbewegung von grossen Molekülen zu beschreiben, die einseitig auf einer Oberfläche festgeheftet sind. Im Vergleich zu den aufwendigen molekulardynamischen (MD) Simulationen grosser Moleküle zeichnet sich die hier dargestellte Methode vor allem durch ihren hohen `Effizienzgewinn' aus, der für komplexe Systeme leicht mehr als fünf Grössenordnungen betragen kann. Dieser Gewinn an Rechenzeit erlaubt bspw. Anwendungen, wie sie mit MD Simulationen wohl auch zukünftig nicht oder nur sehr zögerlich aufgegriffen werden können. Denkbare Anwendungsgebiete dieser Näherung betreffen dabei nicht nur dichte Lösungen, in denen auch die Wechselwirkungen der molekularen beads zu benachbarten Makromolekülen eine Rolle spielt, sondern auch Untersuchungen zu ionischen Flüssigkeiten oder zur Topologie grosser Moleküle.

A semi-quantitative approach to deriving a model structure for the uptake of organic chemicals by vegetation

An expert elicitation exercise was undertaken to determine those components and processes that are most important for modeling plant uptake of organic chemicals. The state of our knowledge of these processes was also assessed. This semi-quantitative analysis allowed the construction of an idealized model with seven compartments; soil bulk, soil water, roots, stem, leaves, fruit, and air. Three main areas were identified further research: 1) the uptake of organic chemicals by fruit; 2) the internal transfer of organic chemicals between plant structures (e.g., stem and leaves); and 3) the transfer via the soil-air-plant pathway. Until new data becomes available to quantify these processes, it is proposed that an equilibrium partitioning approach is used between plant components other than fruit or that models consist of both an edible and inedible compartment.