One often wishes to understand the probability distribution of stochastic data from experiment or computer simulations. However, where no model is given, practitioners must resort to parametric or non-parametric methods in order to gain information about the underlying distribution. Others have used initially a nonparametric estimator in order to understand the underlying shape of a set of data, and then later returned with a parametric method to locate the peaks. However they are interested in estimating spectra, which may have multiple peaks, where in this work we are interested in approximating the peak position of a single-peak probability distribution. One method of analyzing a distribution of data is by fitting a curve to, or smoothing them. Polynomial regression and least-squares fit are examples of smoothing methods. Initial understanding of the underlying distribution can be obscured depending on the degree of smoothing. Problems such as under and oversmoothing must be addressed in order to determine the shape of the underlying distribution. Furthermore, smoothing of skewed data can give a biased estimation of the peak position. We propose two new approaches for statistical mode estimation based on the assumption that the underlying distribution has only one peak. The first method imposes the global constraint of unimodality locally, by requiring negative curvature over some domain. The second method performs a search that assumes a position of the distribution's peak and requires positive slope to the left, and negative slope to the right. Each approach entails a constrained least-squares fit to the raw cumulative probability distribution. We compare the relative efficiencies [12] of finding the peak location of these two estimators for artificially generated data from known families of distributions Weibull, beta, and gamma. Within each family a parameter controls the skewness or kurtosis, quantifying the shapes of the distributions for comparison. We also compare our methods with other estimators such as the kernel-density estimator, adaptive histogram, and polynomial regression. By comparing the effectiveness of the estimators, we can determine which estimator best locates the peak position. We find that our estimators do not perform better than other known estimators. We also find that our estimators are biased. Overall, an adaptation of kernel estimation proved to be the most efficient. The results for the work done in this thesis will be submitted, in a different form, for publication by D.A. Rabson and J.K. Looper.

With increasing globalisation of the world economy, there is growing interest in international business research among academics, business practitioners and public policy makers. As marketing is usually the first corporate function to internationalise, it occupies the centre-stage in the international strategy debate. The objective of this study is to understand the environmental and organisational factors that drive the desirable outcomes of learning, innovation and performance in multinational firms. By adapting the IO-based, resource-based and contingency theories, the study proposes the environment-conduct-outcome framework and a model of global marketing in MNCs. Using the structural equation modelling-based PLS methodology, the model is estimated with data from a global survey of marketing managers in MNC subsidiaries. The results show that the traditional international marketing strategy and organisational structure constructs of adaptation and autonomy do not have a significant direct effect on MNC performance. Instead, the effects are largely mediated by the networking, learning and innovation constructs that are included in the proposed model. The study also shows that, whereas collaborative decision making has a positive effect on interunit learning, subsidiary autonomy has a significant influence on innovativeness in MNC subsidiaries. Finally, it is found that marketing mix adaptation has an adverse impact on the performance of MNCs facing high global integration pressures but improves the performance of MNCs confronted with low global integration pressures. The findings have important implications for global marketing in MNCs. First, to enhance organisational learning and innovation and ultimately improve corporate performance, MNCs should simultaneously develop the potentially conflicting organisational attributes of collective decision-making among the subsidiaries and greater autonomy to the subsidiaries. Second, to tap local knowledge, MNCs should increasingly regard their country units as 'colleges' or 'seminaries' of learning rather than merely as 'subsidiaries' with secondary or subordinate roles. Finally, to improve MNC performance, the key requirement is to achieve a good fit between the global organisational structure, marketing strategy and business environment. Overall, the results provide partial support for the IO-based and resource-based views and strong support for the contingency perspective in international strategy.

The field study examines the effect of the simulation game Battle Squares on the learning of the map reading skill of grid-reference determination by year 7 students. The effect of ability level and sex differences on the acquisition of gridreferencing skills were also examined. The simulation game developed is a modification of the traditional children's game Battleships. The modifications produced the major features of the grid system used on Australian Survey Map sheets without substantially altering the characteristics of the game Battleships. Two treatment groups played the simulation game, one group having experienced both a pre test and a post test and the other group only the post test. A third treatment did the pre test and post test without experiencing the simulation game. Students in both treatment groups which experienced the simulation game showed significant gains in the learning of grid-referencing skills. Students in upper ability level groups gained significantly better scores on the post test than students in lower ability level groups. Both upper and lower ability level groups showed significant gains. Girls performed significantly better than boys on the post test. Both boys and girls showed significant gains as a result of the simulation game experience. Ability level was a more important moderating variable than sex difference in producing variations in performance on the post test of grid-referencing skills. The explicit educational aims of the simulation game were effectively achieved in a short period of time, while maintaining student motivation and interest. The success of the simulation game in producing significant changes in grid referencing skills would appear to have resulted from the frequent practise of these skills the simulation game playing experience offers.

<p>In regression with near collinear explanatory variables, the least squares predictor has large variance. Ordinary least squares regression (OLSR) often leads to unrealistic regression coefficients. Several regularized regression methods have been proposed as alternatives. Well-known are principal components regression (PCR), ridge regression (RR) and continuum regression (CR). The latter two involve a continuous metaparameter, offering additional flexibility.</p><p>For a univariate response variable, CR incorporates OLSR, PLSR, and PCR as special cases, for special values of the metaparameter. CR is also closely related to RR. However, CR can in fact yield regressors that vary discontinuously with the metaparameter. Thus, the relation between CR and RR is not always one-to-one. We develop a new class of regression methods, LSRR, essentially the same as CR, but without discontinuities, and prove that any optimization principle will yield a regressor proportional to a RR, provided only that the principle implies maximizing some function of the regressor's sample correlation coefficient and its sample variance. For a multivariate response vector we demonstrate that a number of well-established regression methods are related, in that they are special cases of basically one general procedure. We try a more general method based on this procedure, with two meta-parameters. In a simulation study we compare this method to ridge regression, multivariate PLSR and repeated univariate PLSR. For most types of data studied, all methods do approximately equally well. There are cases where RR and LSRR yield larger errors than the other methods, and we conclude that one-factor methods are not adequate for situations where more than one latent variable are needed to describe the data. Among those based on latent variables, none of the methods tried is superior to the others in any obvious way.</p>

<p>PolyTech AB in Malmköping manufactures gyro stabilized camera systems or helicopter applications. In this Master´s Thesis a shaker test rig for vibration testing of these systems is designed, implemented and evaluated. The shaker is required to have an adjustable frequency and displacement and different shakers that meet these requirements are treated in a literature study.</p><p>The shaker chosen in the test rig is based on a mechanical solution that is described in detail. Additionally all components used in the test rig are described and modelled. The test rig is identified and evaluated from different experiments carried out at PolyTech, where the major part of the identification is based on data collected from accelerometers.</p><p>The test rig model is used to develop a controller that controls the frequency and the displacement of the shaker. A three-phase motor is used to control the frequency of the shaker and a linear actuator with a servo is used to control the displacement. The servo controller is designed using observer and state feedback techniques.</p><p>Additionally, the mount in which the camera system is hanging is modelled and identified, where the identification method is based on nonlinear least squares (NLS) curve fitting technique.</p>

<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>

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.