Contribution Among Joint Tortfeasors and the Marital Immunity

Estimation of parameters in the classical Growth Curve model when the covariance matrix has some specific linear structure is considered. In our examples maximum likelihood estimators can not be obtained explicitly and must rely on optimization algorithms. Therefore explicit estimators are obtained as alternatives to the maximum likelihood estimators. From a discussion about residuals, a simple non-iterative estimation procedure is suggested which gives explicit and consistent estimators of both the mean and the linear structured covariance matrix.Original Publication:Martin Ohlson and Dietrich von Rosen, Explicit Estimators of Parameters in the Growth Curve Model with Linearly Structured Covariance Matrices, 2010, Journal of Multivariate Analysis, (101), 5, 1284-1295.http://dx.doi.org/10.1016/j.jmva.2009.12.023Copyright: Elsevier Science B.V., Amsterdamhttp://www.elsevier.com

Interrogation of Employees Concerning Union Matters as an Unfair Labor Practice

The problemof estimating parameters of amultivariate normal p-dimensional random vector is considered for a banded covariance structure reflecting mdependence. A simple non-iterative estimation procedure is suggested which gives an explicit, unbiased and consistent estimator of the mean and an explicit and consistent estimator of the covariance matrix for arbitrary p and m.Preliminary version published as Research Report 2008:3 at the Centre of Biostochastics Swedish University of Agricultural Sciences.The original publication is available at www.springerlink.com:Martin Ohlson, Zhanna Andrushchenko and Dietrich von Rosen, Explicit Estimators under m-Dependence for a Multivariate Normal Distribution, 2011, Annals of the Institute of Statistical Mathematics, (63), 1, 29-42.http://dx.doi.org/10.1007/s10463-008-0213-1Copyright: Springer Science Business Mediahttp://www.springerlink.com

Moments of the likelihood-based discriminant function

The likelihood approach used in this paper leads to quadratic discriminant functions. Classification into one of two known multivariate normal populations with a known and unknown covariance matrix are separately considered, where the two cases depend on the sample size and an unknown squared Mahalanobis distance. Their exact distributions are complicated to obtain. Therefore, moments for the likelihood based discriminant functions are established to express the basic characteristics of respective distribution

Estimation of parameters in the extended growth curve model with a linearly structured covariance matrix

In this paper the extended growth curve model with two terms and a linearly structured covariance matrix is considered. We propose an estimation procedure that handles linearly structured covariance matrices. The idea is first to estimate the covariance matrix when finding the inner product in a regression space and thereafter re-estimate it when it should be interpreted as a dispersion matrix. This idea is exploited by decomposing the residual space, the orthogonal complement to the design space, into three orthogonal subspaces. Studying residuals obtained from projections of observations on these subspaces yields explicit consistent estimators of the covariance matrix. An explicit consistent estimator of the mean is also proposed and numerical examples are given

The multilinear normal distribution: Introduction and some basic properties

AbstractIn this paper, the multilinear normal distribution is introduced as an extension of the matrix-variate normal distribution. Basic properties such as marginal and conditional distributions, moments, and the characteristic function, are also presented. A trilinear example is used to explain the general contents at a simpler level. The estimation of parameters using a flip-flop algorithm is also briefly discussed

An Edgeworth-type expansion for the distribution of a likelihood-based discriminant function

The exact distribution of a classification function is often complicated to allow for easy numerical calculations of misclassification errors. The use of expansions is one way of dealing with this difficulty. In this paper, approximate probabilities of misclassification of the maximum likelihood-based discriminant function are established via an Edgeworth-type expansion based on the standard normal distribution for discriminating between two multivariate normal populations

Seizure (Ictal)—EEG Characteristics in Subgroups of Depressive Disorder in Patients Receiving Electroconvulsive Therapy (ECT)—A Preliminary Study and Multivariate Approach

Objectives. Examine frequency distributions of ictal EEG after ECT stimulation in diagnostic subgroups of depression. Methods. EEG registration was consecutively monitored in 33 patients after ECT stimulation. Patients were diagnosed according to DSM IV and subdivided into: (1) major depressive disorder with psychotic features (n = 7), (2) unipolar depression (n = 20), and (3) bipolar depression (n = 6). Results. Results indicate that the diagnostically subgroups differ in their ictal EEG frequency spectrumml: (1) psychotic depression has a high occurrence of delta and theta waves, (2) unipolar depression has high occurrence of delta, theta and gamma waves, and (3) bipolar depression has a high occurrence of gamma waves. A linear discriminant function separated the three clinical groups with an accuracy of 94%. Conclusion. Psychotic depressed patients differ from bipolar depression in their frequency based on probability distribution of ictal EEG. Psychotic depressed patients show more prominent slowing of EEG than nonpsychotic depressed patients. Thus the EEG results may be supportive in classifying subgroups of depression already at the start of the ECT treatment

Количественные критерии гигиенической оценки воздействия на организм многокомпонентного загрязнения атмосферного воздуха

ВОЗДУХВОЗДУХА ЗАГРЯЗНИТЕЛИизоэффективные концентрацииэксперименты на животныхазота диоксидсеры диоксидАЗОТА ОКСИДЫ /вред воз

Strategisk regnskapsanalyse og verdsettelse av Hjellegjerde ASA

Formålet med denne utredningen har vært å finne et anslag på verdien av Hjellegjerde ved hjelp av en fundamental verdsettelsesmodell. Verdsettelsen er basert på ekstern informasjon og den kunnskapen vi har tilegnet oss gjennom siviløkonomstudiet. Utredningen er delt inn i sju kapitler. I første kapittel gir vi en disposisjon over utredningen. Det andre kapittelet inneholder en presentasjon av Hjellegjerde, og kapittel tre omfatter en strategisk analyse, det vil si både en eksternanalyse og en internanalyse. Regnskapsanalysen i kapittel fire består av omgruppering, analyse og justering av målefeil og forholdstallsanalyse. Basert på den strategiske analysen og regnskapsanalysen utarbeider vi et fremtidsregnskap. I kapittel seks benytter vi fundamental verdsettelse til å komme frem til et verdiestimat på Hjellegjerdeaksjen. Estimatet er usikkert og kapittelet består derfor også av sensitivitetsanalyse. Vi avslutter oppgaven med kapittel sju, hvor vi oppsummerer og konkluderer