Sökning: "Linear Covariance Analysis"
Visar resultat 1 - 5 av 13 uppsatser innehållade orden Linear Covariance Analysis.
1. Primary Drivers of Sea Level Variability in the North – Baltic Sea Transition Using Machine Learning
Master-uppsats, Göteborgs universitet/Institutionen för geovetenskaperSammanfattning : Global mean sea level is rising, however not uniformly. Regional deviations of sea surface height (SSH) are common due to local drivers, including surface winds, ocean density stratifications, vertical land- & crustal movements and more. LÄS MER
2. Robustness Against Non-Normality : Evaluating LDA and QDA in Simulated Settings Using Multivariate Non-Normal Distributions
Kandidat-uppsats, Uppsala universitet/Statistiska institutionenSammanfattning : Evaluating classifiers in controlled settings is essential for empirical applications, as extensive knowledge on model-behaviour is needed for accurate predictions. This thesis investigates robustness against non-normality of two prominent classifiers, LDA and QDA. LÄS MER
3. The Black-Litterman Model: An Investigation of Confidence
Magister-uppsats, Lunds universitet/Nationalekonomiska institutionenSammanfattning : This paper examines Idzorek’s extension of the Black-Litterman model with respect to confidence levels and makes a general comparison with the Canonical Reference Model. To test Idzorek’s method a global equities portfolio is constructed using assets representing nine different countries consisting in total of 80. LÄS MER
4. Modelling gross primary production in semi-arid regions: effects on carbon uptake of intensive agriculture in southern Kenya
Master-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskapSammanfattning : Background and aim: Gross primary production (GPP) is the largest global carbon (C) flux and an important component for counteracting anthropogenic CO2 emissions, understanding vegetation dynamics, and sustaining universal human standards. Africa plays a prominent role in the global C cycle, though our understanding of GPP dynamics is largely hampered by a paucity of ground-based observations. LÄS MER
5. Evaluation of the Robustness of Different Classifiers under Low- and High-Dimensional Settings
Master-uppsats, Uppsala universitet/Statistiska institutionenSammanfattning : This thesis compares the performance and robustness of five different varities of discriminant analysis, namely linear (LDA), quadratic (QDA), generalized quadratic (GQDA), diagonal linear (DLDA) and diagonal quadratic (DQDA) discriminant analysis, under elliptical distributions and small sample sizes. By means of simulations, the performance of the classifiers are compared against separation of mean vectors, sample size, number of variables, degree of non-normality and covariance structures. LÄS MER