3 research outputs found
KOLMOGOROV-SMIRNOV TYPE TESTS UNDER SPATIAL CORRELATIONS
Kolmogorov-Smirnov test is a non-parametric hypothesis test that measures the probability of deviations, that the interested univariate random variable is drawn from a pre-speciïŹed distribution (one-sample KS) or has the same distribution as a second random variable (twosample KS). The test is based on the measure of the supremum (greatest) distance between an empirical distribution function (EDF) and a pre-speciïŹed cumulative distribution function (CDF) or the largest distance between two EDFs. KS test has been widely adopted in statistical analysis due to its virtue of more general assumptions compared to parametric test like t-test. In addition, the p-value derived from the KS test is more robust and distribution-free for a large class of random variables. However, the fundamental assumption of independence is usually overlooked and may potentially cause inaccurate inferences. The KS test in its original form assumes the interested random variable to be independently distributed while itâs not true in a lot of nature datasets, especially when we are dealing with more complicated situations like imgage analysis, geostatistical which may involve spatial dependence. I proposed a modiïŹed KS test with adjustment via spatial correlation. The dissertation concerns the following three aims. First, I conducted a systematical review on the KS test, the Cramer von Mise test, the Anderson-Darling test and the Chi-square test and evaluate their performance under normal distributions, Weibull distributions and multinomial distributions. In the review, I also studied how these tests perform when random variables are correlated. Second, I proposed a modiïŹed KS test that corrects the bias in estimating CDF/EDF when spatial dependence exists and calculate the informative sample size. Finally, I conducted a revisit analysis of coronary ïŹow reserve and pixel distribution of coronary ïŹow capacity by Kolmogorov-Smirnov with spatial correction to evaluate the efïŹciency of dipyridamole and regadenoson
Objective measures of complexity
Mesures Objectives de la ComplexitĂ© pour la Prise de DĂ©cision Dynamique. La gestion efficace de systĂšmes sociotechniques complexes dĂ©pend dâune comprĂ©hension des interrelations dynamiques entre les composantes de ces systĂšmes, de leur Ă©volution Ă travers le temps, ainsi que du degrĂ© dâincertitude auquel les dĂ©cideurs sont exposĂ©s. Quelles sont les caractĂ©ristiques de la prise de dĂ©cision complexe qui ont un impact sur la performance humaine dans lâenvironnement moderne du travail, constamment en fluctuation et sous la pression du temps, exerçant de lourdes demandes sur la cognition ? La prise de dĂ©cision complexe est un concept issu de la macrocognition, impliquant des processus et des fonctions de bas et haut niveaux de description tels que la mĂ©tacognition, soit pour un individu de penser Ă propos de son propre processus de pensĂ©es. Dans le cas particulier de la prise de dĂ©cision complexe, ce phĂ©nomĂšne est nommĂ© la pensĂ©e systĂ©mique. LâĂ©tude de la prise de dĂ©cision complexe en dehors de lâenvironnement traditionnel du laboratoire, permettant un haut niveau de contrĂŽle mais un faible degrĂ© de rĂ©alisme, est malheureusement difficile et presque impossible. Une mĂ©thode de recherche plus appropriĂ©e pour la macrocognition est lâexpĂ©rimentation basĂ©e sur la simulation, Ă lâaide de micromondes numĂ©risĂ©s sous la forme de jeux sĂ©rieux. Ce paradigme de recherche est nommĂ© la prise de dĂ©cision dynamique (PDD), en ce quâil tient compte des caractĂ©ristiques de problĂšmes de prise de dĂ©cision complexe telles que des sĂ©quences complexes de dĂ©cisions et de changements dâĂ©tats dâun problĂšme interdĂ©pendants, qui peuvent changer de façon spontanĂ©e ou comme consĂ©quence de dĂ©cisions prĂ©alables, et pour lesquels la connaissance et la comprĂ©hension du dĂ©cideur peut nâĂȘtre que partielle ou incertaine. MalgrĂ© la quantitĂ© de recherche concernant la PDD Ă propos des difficultĂ©s encourues pour la performance humaine face Ă des problĂšmes de prise de dĂ©cision complexe, lâacquisition de connaissances Ă propos de systĂšmes complexes, et Ă savoir si le transfert de lâapprentissage est possible, il nâexiste pas de mesure quantitative de ce en quoi un problĂšme de dĂ©cision est considĂ©rĂ© comme Ă©tant complexe. La littĂ©rature scientifique mentionne des Ă©lĂ©ments qualitatifs concernant les systĂšmes complexes (tels que des interrelations dynamiques, une Ă©volution non-linĂ©aire dâun systĂšme Ă travers le temps, et lâincertitude Ă propos des Ă©tats dâun systĂšme et des issues des dĂ©cisions), mais des mesures quantitatives et objectives exprimant la complexitĂ© de problĂšmes de dĂ©cision nâont pas Ă©tĂ© dĂ©veloppĂ©es. Cette dissertation doctorale prĂ©sente les concepts, la mĂ©thodologie et les rĂ©sultats impliquĂ©s dans un projet de recherche visant Ă dĂ©velopper des mesures objectives de la complexitĂ© basĂ©es sur les caractĂ©ristiques de problĂšmes de prise de dĂ©cision dynamique pouvant expliquer et prĂ©dire la performance humaine. En sâinspirant de divers domaines dâapplication de la thĂ©orie de la complexitĂ© tels que la complexitĂ© computationnelle, la complexitĂ© systĂ©mique, et lâinformatique cognitive, un modĂšle formel des paramĂštre de la complexitĂ© pour des tĂąches de prise de dĂ©cision dynamique a Ă©tĂ© Ă©laborĂ©. Un ensemble de dix mesures objectives de la complexitĂ© a Ă©tĂ© dĂ©veloppĂ©, consistant en des mesures de la complexitĂ© structurelle, des mesures de la complexitĂ© informationnelle, la complexitĂ© de la charge cognitive, et des mesures de la difficultĂ© dâun problĂšme, de la non-linĂ©aritĂ© des relations, de lâincertitude concernant lâinformation et les dĂ©cisions, ainsi quâune mesure de lâinstabilitĂ© dâun systĂšme dynamique sous des conditions dâinertie. Une analyse des rĂ©sultats expĂ©rimentaux colligĂ©s Ă partir de cinq scĂ©narios de PDD rĂ©vĂšle quâun nombre restreint de candidats parmi des modĂšles de rĂ©gression linĂ©aires multiple permet dâexpliquer et de prĂ©dire les rĂ©sultats de performance humaine, mais au prix de certaines violations des postulats de lâapproche classique de la rĂ©gression linĂ©aire. De plus, ces mesures objectives de la complexitĂ© prĂ©sentent un degrĂ© Ă©levĂ© de multicolinĂ©aritĂ©, causĂ©e dâune part par lâinclusion de caractĂ©ristiques redondantes dans les calculs, et dâautre part par une colinĂ©aritĂ© accidentelle imputable Ă la conception des scĂ©narios de PDD. En tenant compte de ces deux considĂ©rations ainsi que de la variance Ă©levĂ©e observĂ©e dans les processus macrocognitifs impliquĂ©s dans la prise de dĂ©cision complexe, ces modĂšles prĂ©sentent des valeurs Ă©levĂ©es pour le terme dâerreur exprimant lâĂ©cart entre les observations et les prĂ©dictions des modĂšles. Une analyse additionnelle explore lâutilisation de mĂ©thodes alternatives de modĂ©lisation par rĂ©gression afin de mieux comprendre la relation entre les paramĂštres de la complexitĂ© et les donnĂ©es portant sur performance humaine. Nous avons dâabord optĂ© pour une approche de rĂ©gression robuste afin dâaugmenter lâefficience de lâanalyse de rĂ©gression en utilisant une mĂ©thode rĂ©duisant la sensibilitĂ© des modĂšles de rĂ©gression aux observations influentes. Une seconde analyse Ă©limine la source de variance imputable aux diffĂ©rences individuelles en focalisant exclusivement sur les effets imputables aux conditions expĂ©rimentales. Une derniĂšre analyse utilise des modĂšles non-linĂ©aires et non-paramĂ©triques afin de pallier les postulats de la modĂ©lisation par rĂ©gression, Ă lâaide de mĂ©thodes dâapprentissage automatique (machine learning). Les rĂ©sultats suggĂšrent que lâapproche de rĂ©gression robuste produit des termes dâerreur substantiellement plus faibles, en combinaison avec des valeurs Ă©levĂ©es pour les mesures de variance expliquĂ©e dans les donnĂ©es de la performance humaine. Bien que les mĂ©thodes non-linĂ©aires et non-paramĂ©triques produisent des modĂšles marginalement plus efficients en comparaison aux modĂšles de rĂ©gression linĂ©aire, la combinaison de ces modĂšles issus du domaine de lâapprentissage automatique avec les donnĂ©es restreintes aux effets imputables aux conditions expĂ©rimentales produit les meilleurs rĂ©sultats relativement Ă lâensemble de lâeffort de modĂ©lisation et dâanalyse de rĂ©gression. Une derniĂšre section prĂ©sente un programme de recherche conçu pour explorer lâespace des paramĂštres pour les mesures objectives de la complexitĂ© avec plus dâampleur et de profondeur, afin dâapprĂ©hender les combinaisons des caractĂ©ristiques des problĂšmes de prise de dĂ©cision complexe qui sont des facteurs dĂ©terminants de la performance humaine. Les discussions concernant lâapproche expĂ©rimentale pour la PDD, les rĂ©sultats de lâexpĂ©rimentation relativement aux modĂšles de rĂ©gression, ainsi quâĂ propos de lâinvestigation de mĂ©thodes alternatives visant Ă rĂ©duire la composante de variance menant Ă la disparitĂ© entre les observations et les prĂ©dictions des modĂšles suggĂšrent toutes que le dĂ©veloppement de mesures objectives de la complexitĂ© pour la performance humaine dans des scĂ©narios de prise de dĂ©cision dynamique est une approche viable Ă lâapprofondissement de nos connaissances concernant la comprĂ©hension et le contrĂŽle exercĂ©s par un ĂȘtre humain face Ă des problĂšmes de dĂ©cision complexe.Objective Measures of Complexity for Dynamic Decision-Making. Managing complex sociotechnical systems depends on an understanding of the dynamic interrelations of such systemsâ components, their evolution over time, and the degree of uncertainty to which decision makers are exposed. What features of complex decision-making impact human performance in the cognitively demanding, ever-changing and time pressured modern workplaces? Complex decision-making is a macrocognitive construct, involving low to high cognitive processes and functions, such as metacognition, or thinking about oneâs own thought processes. In the particular case of complex decision-making, this is called systems thinking. The study of complex decision-making outside of the controlled, albeit lacking in realism, traditional laboratory environment is difficult if not impossible. Macrocognition is best studied through simulation-based experimentation, using computerized microworlds in the form of serious games. That research paradigm is called dynamic decision-making (DDM), as it takes into account the features of complex decision problems, such as complex sequences of interdependent decisions and changes in problem states, which may change spontaneously or as a consequence of earlier decisions, and for which the knowledge and understanding may be only partial or uncertain. For all the research in DDM concerning the pitfalls of human performance in complex decision problems, the acquisition of knowledge about complex systems, and whether a learning transfer is possible, there is no quantitative measure of what constitutes a complex decision problem. The research literature mentions the qualities of complex systems (a systemâs dynamical relationships, the nonlinear evolution of the system over time, and the uncertainty about the system states and decision outcomes), but objective quantitative measures to express the complexity of decision problems have not been developed. This dissertation presents the concepts, methodology, and results involved in a research endeavor to develop objective measures of complexity based on characteristics of dynamic decision-making problems which can explain and predict human performance. Drawing on the diverse fields of application of complexity theory such as computational complexity, systemic complexity, and cognitive informatics, a formal model of the parameters of complexity for dynamic decision-making tasks has been elaborated. A set of ten objective measures of complexity were developed, ranging from structural complexity measures, measures of information complexity, the cognitive weight complexity, and measures of problem difficulty, nonlinearity among relationships, information and decision uncertainty, as well as a measure of the dynamical systemâs instability under inertial conditions. An analysis of the experimental results gathered using five DDM scenarios revealed that a small set of candidate models of multiple linear regression could explain and predict human performance scores, but at the cost of some violations of the assumptions of classical linear regression. Additionally, the objective measures of complexity exhibited a high level of multicollinearity, some of which were caused by redundant feature computation while others were accidentally collinear due to the design of the DDM scenarios. Based on the aforementioned constraints, and due to the high variance observed in the macrocognitive processes of complex decision-making, the models exhibited high values of error in the discrepancy between the observations and the model predictions. Another exploratory analysis focused on the use of alternative means of regression modeling to better understand the relationship between the parameters of complexity and the human performance data. We first opted for a robust regression analysis to increase the efficiency of the regression models, using a method to reduce the sensitivity of candidate regression models to influential observations. A second analysis eliminated the within-treatment source of variance in order to focus exclusively on between-treatment effects. A final analysis used nonlinear and non-parametric models to relax the regression modeling assumptions, using machine learning methods. It was found that the robust regression approach produced substantially lower error values, combined with high measures of the variance explained for the human performance data. While the machine learning methods produced marginally more efficient models of regression for the same candidate models of objective measures of complexity, the combination of the nonlinear and non-parametric methods with the restricted between-treatment dataset yielded the best results of all of the modeling and analyses endeavors. A final section presents a research program designed to explore the parameter space of objective measures of complexity in more breadth and depth, so as to weight which combinations of the characteristics of complex decision problems are determinant factors on human performance. The discussions about the experimental approach to DDM, the experimental results relative to the regression models, and the investigation of further means to reduce the variance component underlying the discrepancy between the observations and the model predictions all suggest that establishing objective measures of complexity for human performance in dynamic decision-making scenarios is a viable approach to furthering our understanding of a decision makerâs comprehension and control of complex decision problems
Latvijas UniversitÄtes 2013.gada publiskais pÄrskats
Ć ajÄ pÄrskatÄ ir apvienoti: 1. Gada publiskais pÄrskats (LR Likuma par budĆŸetu un finanĆĄu vadÄ«bu 14.panta 3.punkts; 05.05.2010. Ministru kabineta noteikumi Nr.413 âNoteikumi par gada publiskajiem pÄrskatiemâ); 2. ZinÄtniskÄs institĆ«cijas gada publiskais pÄrskats (LR ZinÄtniskÄs darbÄ«bas likuma 40.pants; 16.05.2006. Ministru kabineta noteikumi Nr.397 âNoteikumi par zinÄtnisko institĆ«ciju reÄŁistrÄ reÄŁistrÄtÄ zinÄtniskÄ institĆ«ta gada publisko pÄrskatuâ); 3. GadagrÄmata (LR Augstskolu likuma 75.pants). PÄrskats ir sagatavots, izmantojot datus no LU fakultÄĆĄu, zinÄtnisko institĆ«tu, studiju centru un LU aÄŁentĆ«ru iesniegtajiem pÄrskatiem, LUIS datus, kÄ arÄ« LU administratÄ«vo un patstÄvÄ«go akadÄmisko struktĆ«rvienÄ«bu sagatavotos materiÄlus