<p>Vid analys av vattenprover tagna från t.ex. ett vattendrag betäms halten av olika ämnen. Dessa halter är ofta beroende av vattenföringen. Det är av intresse att ta reda på om observerade förändringar i halterna beror på naturliga variationer eller är orsakade av andra faktorer. För att undersöka detta har föreslagits en statistisk tidsseriemodell som innehåller okända parametrar. Modellen anpassas till uppmätta data vilket leder till ett underbestämt ekvationssystem. I avhandlingen studeras bl.a. olika sätt att säkerställa en unik och rimlig lösning. Grundidén är att införa vissa tilläggsvillkor på de sökta parametrarna. I den studerade modellen kan man t.ex. kräva att vissa parametrar inte varierar kraftigt med tiden men tillåter årstidsvariationer. Det görs genom att dessa parametrar i modellen regulariseras.</p><p>Detta ger upphov till ett minsta kvadratproblem med en eller två regulariseringsparametrar. I och med att inte alla ingående parametrar regulariseras får vi dessutom ett partiellt regulariserat minsta kvadratproblem. I allmänhet känner man inte värden på regulariseringsparametrarna utan problemet kan behöva lösas med flera olika värden på dessa för att få en rimlig lösning. I avhandlingen studeras hur detta problem kan lösas numeriskt med i huvudsak två olika metoder, en iterativ och en direkt metod. Dessutom studeras några sätt att bestämma lämpliga värden på regulariseringsparametrarna.</p><p>I en iterativ lösningsmetod förbättras stegvis en given begynnelseapproximation tills ett lämpligt valt stoppkriterium blir uppfyllt. Vi använder här konjugerade gradientmetoden med speciellt konstruerade prekonditionerare. Antalet iterationer som krävs för att lösa problemet utan prekonditionering och med prekonditionering jämförs både teoretiskt och praktiskt. Metoden undersöks här endast med samma värde på de två regulariseringsparametrarna.</p><p>I den direkta metoden används QR-faktorisering för att lösa minsta kvadratproblemet. Idén är att först utföra de beräkningar som kan göras oberoende av regulariseringsparametrarna samtidigt som hänsyn tas till problemets speciella struktur.</p><p>För att bestämma värden på regulariseringsparametrarna generaliseras Reinsch’s etod till fallet med två parametrar. Även generaliserad korsvalidering och en mindre beräkningstung Monte Carlo-metod undersöks.</p> / <p>Statistical analysis of data from rivers deals with time series which are dependent, e.g., on climatic and seasonal factors. For example, it is a well-known fact that the load of substances in rivers can be strongly dependent on the runoff. It is of interest to find out whether observed changes in riverine loads are due only to natural variation or caused by other factors. Semi-parametric models have been proposed for estimation of time-varying linear relationships between runoff and riverine loads of substances. The aim of this work is to study some numerical methods for solving the linear least squares problem which arises.</p><p>The model gives a linear system of the form <em>A</em><em>1x1</em><em> + A</em><em>2x2</em><em> + n = b</em><em>1</em>. The vector <em>n</em> consists of identically distributed random variables all with mean zero. The unknowns, <em>x,</em> are split into two groups, <em>x</em><em>1</em><em> </em>and <em>x</em><em>2</em><em>.</em> In this model, usually there are more unknowns than observations and the resulting linear system is most often consistent having an infinite number of solutions. Hence some constraint on the parameter vector x is needed. One possibility is to avoid rapid variation in, e.g., the parameters<em> x</em><em>2</em><em>.</em> This can be accomplished by regularizing using a matrix <em>A</em><em>3</em>, which is a discretization of some norm. The problem is formulated</p><p>as a partially regularized least squares problem with one or two regularization parameters. The parameter <em>x</em><em>2</em> has here a two-dimensional structure. By using two different regularization parameters it is possible to regularize separately in each dimension.</p><p>We first study (for the case of one parameter only) the conjugate gradient method for solution of the problem. To improve rate of convergence blockpreconditioners of Schur complement type are suggested, analyzed and tested. Also a direct solution method based on QR decomposition is studied. The idea is to first perform operations independent of the values of the regularization parameters. Here we utilize the special block-structure of the problem. We further discuss the choice of regularization parameters and generalize in particular Reinsch’s method to the case with two parameters. Finally the cross-validation technique is treated. Here also a Monte Carlo method is used by which an approximation to the generalized cross-validation function can be computed efficiently.</p>

<p>Jag använder ett från Världsbanken nyligen utkommet datamaterial över institutionell kvalitet i världens länder för att i en replikeringsstudie undersöka sambandet mellan institutionell utveckling och ekonomisk tillväxt. Modellen har med framgång redan tidigare använts, men i detta arbete är tidsperioden en senare och datamaterialet enligt min bedömning av högre kvalitet. För att kunna göra det senare uttalandet och analysera resultaten på ett uttömmande sätt, innefattar arbetet en översiktlig presentation av institutionella teorier. Eftersom undersökt samband i utgångsläget antas uppvisa dubbelriktad kausalitet, använder jag ett ekonometriskt tillvägagångssätt innehållande instrumentering för att trygga validiteten. Sammantaget visar resultaten en enkelriktad, positiv kausaleffekt från institutionell kvalitet till ekonomisk tillväxt. Det är en bit kvar till en verkligt fruktbar modellkonstruktion, samtidigt som arbetet pekar på att institutioner hör hemma i en sådan.</p>

Monitoring reproductive success, prey species composition, and colony size of marine birds has been proposed as a method of assessing changes in marine systems that are otherwise difficult to sample (Cairns 1987). I measured inter-annual and intra-seasonal variability in reproductive parameters, taxonomic composition of the diet, and adult body condition of Crested Auklets (Aethia cristatella) and Least Auklets (A. pusilla) at 2 colonies near the village of Savoonga, St. Lawrence Island, Alaska during the 2000-2002 breeding seasons to evaluate how reproductive success of planktivorous seabirds is related to diet. I also assessed the utility of two methods of population monitoring (surface counts and mark-resighting) for detecting annual changes in breeding populations of Crested and Least auklets during the 2001 and 2002 breeding seasons on the Kitnik colony. Average reproductive success was generally high (>60% of nests) for both auklet species during the 3 years of the study, but differed among years. Median hatching dates for both species were 2 weeks earlier in the year of highest reproductive success (2002), compared to the previous 2 years. In all 3 years, the diet of Crested Auklets was predominantly euphausiids, while the diet of Least Auklets consisted primarily of calanoid copepods, but species composition of the diet differed among years for both species. Crested and Least auklets consumed more of the large, lipid-rich copepod Neocalanus cristatus in 2002 than in the other 2 years of the study. The year of lowest reproductive success (2001) was associated with low prevalence of euphausiids in Crested Auklet diets late in the chick-rearing period and high prevalence of the small, low-lipid copepod Calanus marshallae in Least Auklet diets. I observed an increase in total body mass of Crested Auklets during the 2002 breeding season, whereas total body mass declined through the breeding season in the other 2 years. Seasonal changes in adult body mass of Crested Auklets may, therefore, be a useful indicator of food availability. Average body mass of Least Auklets declined in all 3 years, but was lowest in 2001, suggesting that low adult body mass of Least Auklets may reflect poor foraging conditions. Fat reserves of breeding auklets during egg-laying were not highly variable among or within breeding seasons and therefore were not a sensitive predictor of subsequent breeding success. Counts of Crested Auklets in plots on the colony surface were highest in areas of large average boulder size; Least Auklet surface counts were not as variable among plots. Maximum counts of both species of auklets in plots did not differ between years. Patterns of colony surface attendance during the breeding season, however, did differ between years. The colony surface attendance of both auklet species after hatching was higher in the year of high reproductive success. Preventing nest initiation by covering plots with tarps did not reduce subsequent colony surface attendance during chick-rearing (after the tarps were removed) for either species, suggesting that reproductive success, independent of differences in food availability, did not cause a difference in colony surface attendance. I estimated abundance of Least Auklets nesting in two 100-m�� plots using mark-resight methods. I concluded that surface counts may provide an indication of among-year differences in colony attendance, but underestimate the number of breeding individuals by a factor of 10. Mark-resighting techniques show more promise for detecting changes in the number of breeding pairs. Reproductive success, adult body mass, and post-hatch colony attendance of Crested and Least auklets appear positively associated with zooplankton availability, particularly the prevalence of N. cristatus in the diet. Annual monitoring of these 3 parameters, together with diet composition, are important for understanding how both natural and anthropogenic climate change may affect trophic structure of the northern Bering Sea ecosystem. / Graduation date: 2004

by Kenneth M. Keverian, Nils R. Sandell, Jr. / Office of Naval Research Contract ONR/N00014-77-C-0532 (85552). / Originally presented as the first author's thesis, (B.S.) in the M.I.T. Dept. of Electrical Engineering and Computer Science, 1979. / Bibliography: p. 114-115.

Webster Pope Dove. / Originally published as thesis (Dept. of Electrical Engineering and Computer Science, M.S., 1980). / Bibliography: leaf 150. / National Science Foundation Grant ENG76-24117 National Science Foundation Grant ECS79-15226

We consider the application of least-squares variational principles and the finite element method to the numerical solution of boundary value problems arising in the fields of solidand fluidmechanics.For manyof these problems least-squares principles offer many theoretical and computational advantages in the implementation of the corresponding finite element model that are not present in the traditional weak form Galerkin finite element model.Most notably, the use of least-squares principles leads to a variational unconstrained minimization problem where stability conditions such as inf-sup conditions (typically arising in mixed methods using weak form Galerkin finite element formulations) never arise. In addition, the least-squares based finite elementmodelalways yields a discrete system ofequations witha symmetric positive definite coeffcientmatrix.These attributes, amongst manyothers highlightedand detailed in this work, allow the developmentofrobust andeffcient finite elementmodels for problems of practical importance. The research documented herein encompasses least-squares based formulations for incompressible and compressible viscous fluid flow, the bending of thin and thick plates, and for the analysis of shear-deformable shell structures.

The focus of this study is to examine the factors that lead to growth in small firms in a Least Developed Country (LDC). The research is based on the manufacturing sector in Ghana. The main objectives of the research are to identify the key variables that lead to small firms' growth and to ascertain the critical barriers that impede growth. A research model which is developed out of an initial exploratory research and existing literature focuses on how the characteristics of the owner/manager, the characteristics of the firm and the business strategy variables interact to affect growth in employment. In addition factors that are perceived to have constrained the growth of the small firms during the study period are ascertained and discussed. To properly test the hypotheses developed a face to face interview survey involving 122 owner/managers of small manufacturing firms is conducted. This resulted in a range of variables that allowed for the construction of a comprehensive multivariate model of small firm growth. A resulting regression model provides about 68 percent of the explanation for the growth of the small firms sampled. It also indicates that the owner/manager characteristics variables offer the most powerful explanation to small firm growth. We find that the owner/manager's growth aspiration is the most influential factor in achieving growth. The other owner/manager characteristics variables that have positive influence on growth are level of education, prior industry experience and entrepreneurial family background. Owner/managers with local experience and/or with other business interests are less likely to achieve faster growth. Foreign owned/managed firms grow faster. Younger and smaller firms appear to grow faster. While firms with multiple ownerships tend to grow at a slower rate than firms owned and managed by one person. Business planning, marketing and export have positive and significant impacts on growth. Other business strategies such as innovations and staff training also have direct relationships with growth but not significant. Some of the main constraining factors to growth are cost of borrowing, lack of access to credit, high cost of inputs, lack of trust within the business community, high bureaucracy, late payments and lack of efficient support system. While the external environment plays important role in small firm growth and development, the behaviours, response and strategies pursued by individual owner/manager are significant factors that determine the rate at which a firm will grow. / Ghana Leasing Company Limited

The dependence between survival times and covariates is described e.g. by proportional hazard models. We consider partly parametric Cox models and discuss here the estimation of interesting parameters. We represent the ma- ximum likelihood approach and extend the results of Huang (1999) from linear to nonlinear parameters. Then we investigate the least squares esti- mation and formulate conditions for the a.s. boundedness and consistency of these estimators.

Vid analys av vattenprover tagna från t.ex. ett vattendrag betäms halten av olika ämnen. Dessa halter är ofta beroende av vattenföringen. Det är av intresse att ta reda på om observerade förändringar i halterna beror på naturliga variationer eller är orsakade av andra faktorer. För att undersöka detta har föreslagits en statistisk tidsseriemodell som innehåller okända parametrar. Modellen anpassas till uppmätta data vilket leder till ett underbestämt ekvationssystem. I avhandlingen studeras bl.a. olika sätt att säkerställa en unik och rimlig lösning. Grundidén är att införa vissa tilläggsvillkor på de sökta parametrarna. I den studerade modellen kan man t.ex. kräva att vissa parametrar inte varierar kraftigt med tiden men tillåter årstidsvariationer. Det görs genom att dessa parametrar i modellen regulariseras. Detta ger upphov till ett minsta kvadratproblem med en eller två regulariseringsparametrar. I och med att inte alla ingående parametrar regulariseras får vi dessutom ett partiellt regulariserat minsta kvadratproblem. I allmänhet känner man inte värden på regulariseringsparametrarna utan problemet kan behöva lösas med flera olika värden på dessa för att få en rimlig lösning. I avhandlingen studeras hur detta problem kan lösas numeriskt med i huvudsak två olika metoder, en iterativ och en direkt metod. Dessutom studeras några sätt att bestämma lämpliga värden på regulariseringsparametrarna. I en iterativ lösningsmetod förbättras stegvis en given begynnelseapproximation tills ett lämpligt valt stoppkriterium blir uppfyllt. Vi använder här konjugerade gradientmetoden med speciellt konstruerade prekonditionerare. Antalet iterationer som krävs för att lösa problemet utan prekonditionering och med prekonditionering jämförs både teoretiskt och praktiskt. Metoden undersöks här endast med samma värde på de två regulariseringsparametrarna. I den direkta metoden används QR-faktorisering för att lösa minsta kvadratproblemet. Idén är att först utföra de beräkningar som kan göras oberoende av regulariseringsparametrarna samtidigt som hänsyn tas till problemets speciella struktur. För att bestämma värden på regulariseringsparametrarna generaliseras Reinsch’s etod till fallet med två parametrar. Även generaliserad korsvalidering och en mindre beräkningstung Monte Carlo-metod undersöks. / Statistical analysis of data from rivers deals with time series which are dependent, e.g., on climatic and seasonal factors. For example, it is a well-known fact that the load of substances in rivers can be strongly dependent on the runoff. It is of interest to find out whether observed changes in riverine loads are due only to natural variation or caused by other factors. Semi-parametric models have been proposed for estimation of time-varying linear relationships between runoff and riverine loads of substances. The aim of this work is to study some numerical methods for solving the linear least squares problem which arises. The model gives a linear system of the form A1x1 + A2x2 + n = b1. The vector n consists of identically distributed random variables all with mean zero. The unknowns, x, are split into two groups, x1 and x2. In this model, usually there are more unknowns than observations and the resulting linear system is most often consistent having an infinite number of solutions. Hence some constraint on the parameter vector x is needed. One possibility is to avoid rapid variation in, e.g., the parameters x2. This can be accomplished by regularizing using a matrix A3, which is a discretization of some norm. The problem is formulated as a partially regularized least squares problem with one or two regularization parameters. The parameter x2 has here a two-dimensional structure. By using two different regularization parameters it is possible to regularize separately in each dimension. We first study (for the case of one parameter only) the conjugate gradient method for solution of the problem. To improve rate of convergence blockpreconditioners of Schur complement type are suggested, analyzed and tested. Also a direct solution method based on QR decomposition is studied. The idea is to first perform operations independent of the values of the regularization parameters. Here we utilize the special block-structure of the problem. We further discuss the choice of regularization parameters and generalize in particular Reinsch’s method to the case with two parameters. Finally the cross-validation technique is treated. Here also a Monte Carlo method is used by which an approximation to the generalized cross-validation function can be computed efficiently.

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