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Road profiles are studied from a vehicle fatigue point of view. A wide range of roads have been measured: from smooth motorways to very rough gravel roads. It is observed that the road profiles consist of irregular sections, which makes the stationary Gaussian model unsuitable (Paper A). In Paper B, a method for automatic identification of such irregularities is presented. It is verified that the irregular sections cause the major part of the fatigue damage induced in vehicles. Based on this result, a new single track model is proposed, which includes randomly shaped and located irregularities. In Paper C, an evaluation method of single track models is proposed. This evaluation method is extended to models of parallel tracks in Paper D. A new "parallel tracks" model is proposed and evaluated accordingly. In Paper E, the coherence between the parallel road tracks is studied. A simple one-parametric model is proposed for the coherence. In Paper F a new theoretical method to compute the expected vehicle fatigue damage caused by road irregularities is presented. Ojämnheter i vägen orsakar påkänningar i åkande fordon. För fordonstillverkare är det intressant att veta egenskaperna hos dessa vägojämnheter. Det är lämpligt att använda statistiska metoder för att analysera mätningar av vägytor/vägprofiler, dels eftersom mätningarna ger stora mängder data och dels eftersom vägytor har ett slumpmässigt beteende (från en 100 meter lång vägprofil kan vi inte exakt prediktera nästa 100 meter). I den här studien har profilerna för flera olika typer av vägar i Sverige uppmätts, alltifrån ojämna grusvägar till jämna motorvägar. Målet är att utveckla statistiska metoder som är lämpliga att använda för analys av sådana mätningar. Totalt har 20 olika vägar mätts, med en total sträcka på 52 mil. De uppmätta vägarna innehåller mycket ojämna avsnitt, vilket medför att en stationär Gaussprocess inte beskriver mätningarna på ett bra sätt (uppsats A). Uppsats B presenterar en ny metod för att identifiera sådana ojämna avsnitt. Det verifieras att dessa ojämnheter har en stor påverkan på fordonsutmattning. Baserat på detta resultat så föreslås en ny statistisk modell för beskrivning av vägprofiler, som inkluderar ojämnheter med slumpmässig form, position och längd. Uppsats C beskriver en ny metod för att utvärdera stokastiska modeller av vägprofiler. Den här utvärderingsmetoden utvidgas till att hantera modeller av två parallella vägprofiler i uppsats D. En ny "parallell-spårs" modell föreslås och utvärderas. Uppsats E fokuserar på koherensen mellan två parallella hjulspår och en enkel parametrisk koherensmodell föreslås. Slutligen, uppsats F beskriver en ny metod för att beräkna den förväntade skadan från en väg som innehåller ojämnheter.

This thesis consists of an extensive introduction followed by seven papers (A-F) on low-level analysis of microarray data. Focus is on calibration and normalization of observed data. The introduction gives a brief background of the microarray technology and its applications in order for anyone not familiar with the field to read the thesis. Formal definitions of calibration and normalization are given. Paper A illustrates a typical statistical analysis of microarray data with background correction, normalization, and identification of differentially expressed genes (among thousands of candidates). A small analysis on the final results for different number of replicates and different image analysis software is also given. Paper B introduces a novel way for displaying microarray data called the print-order plot, which displays data in the order the corresponding spots were printed to the array. Utilizing these, so called (microtiter-) plate effects are identified. Then, based on a simple variability measure for replicated spots across arrays, different normalization sequences are tested and evidence for the existence of plate effects are claimed. Paper C presents an object-oriented extension with transparent reference variables to the R language. It is provides the necessary foundation in order to implement the microarray analysis package described in Paper F. Paper D is on affine transformations of two-channel microarray data and their effects on the log-ratio log-intensity transform. Affine transformations, that is, the existence of channel biases, can explain commonly observed intensity-dependent effects in the log-ratios. In the light of the affine transformation, several normalization methods are revisited. At the end of the paper, a new robust affine normalization is suggested that relies on iterative reweighted principal component analysis. Paper E suggests a multiscan calibration method where each array is scanned at various sensitivity levels in order to uniquely identify the affine transformation of signals that the scanner and the image-analysis methods introduce. Observed data strongly support this method. In addition, multiscan-calibrated data has an extended dynamical range and higher signal-to-noise levels. This is real-world evidence for the existence of affine transformations of microarray data. Paper F describes the aroma package – An R Object-oriented Microarray Analysis environment – implemented in R and that provides easy access to our and others low-level analysis methods. Paper G provides an calibration method for spotted microarrays with dilution series or spike-ins. The method is based on a heteroscedastic affine stochastic model. The parameter estimates are robust against model misspecification.