Spelling suggestions: "subject:"discriminant analysis"" "subject:"oiscriminant analysis""
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An analysis of repeated measurements on experimental units in a two-way classificationMcNee, Richard Cameron 16 February 2010 (has links)
In experiments with repeated measurements made on the same subjects, the repeated observations in time may be correlated. Therefore, the assumption of independent observations cannot be made in general. This thesis considers the experimental design with treatments in a two-way classification with a disproportionate number of subjects allocated to each treatment combination and repeated measurements made on the subjects.
A procedure is shown to be applicable for computing an analysis under somewhat restrictive assumptions. It is assumed that the variances are equal for all times and the correlations in time are equal. The tests obtained are for the three-factor interaction, the two-factor interactions assuming the three-factor interaction zero, and the main effects assuming all interactions zero. The procedure requires the inverse of one matrix, some matrix multiplication, and the calculation of some standard sums of squares. / Master of Science
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Comparison of Discrimination between Logistic Model with Distance Indicator and Regularized Function for Cardiology Ultrasound in Left VentricleKao, Li-wen 08 July 2011 (has links)
Most of the cardiac structural abnormalities will be examined by echocardiography. With more understanding of heart diseases, it is commonly recognized that heart failures are closely related to left ventricular systolic and diastolic functions. This work discusses the association between gray-scale differences and the risk of heart disease from the changes in left ventricular systole and diastole of ultrasound image. Owing to the large dimension
of data matrix, following Chen (2011), we also simplify the influence factors by factor analysis and calculate factor scores to present the characteristics of subjects.
Two kinds of classification criteria are used in this work, namely logistic model with distance indicator and discriminant function. According to Guo et al. (2001), we calculate the Mahalanobis distance from each subject to the center of normal and abnormal group, then use logistic model to fit the distances for classification later. This is called logistic model with distance indicator. For the discriminant analysis, the regularized method by Friedman (1989) for estimation of covariance matrix is used, which is more flexible and can improve the covariance matrix estimates when the sample size is small. As far as the
cut-point of ROC curve, following the approach as in Hanley et al. (1982), we find the most appropriate cut-point which has good performances for both sensitivity and specificity under the same classification criteria. Then the regularized method and the cut-point of ROC curve are combined to be a new classification criterion. The results under the new
classification criterion are presented to classify normal and abnormal groups.
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A multiple discriminate analysis of Elizabethan keyboard variationsSchenck-Hamlin, Donna. January 1984 (has links)
Call number: LD2668 .T4 1984 S335 / Master of Music / Music, Theatre, and Dance
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Variable selection for kernel methods with application to binary classificationOosthuizen, Surette 03 1900 (has links)
Thesis (PhD (Statistics and Actuarial Science))—University of Stellenbosch, 2008. / The problem of variable selection in binary kernel classification is addressed in this thesis.
Kernel methods are fairly recent additions to the statistical toolbox, having originated
approximately two decades ago in machine learning and artificial intelligence. These
methods are growing in popularity and are already frequently applied in regression and
classification problems.
Variable selection is an important step in many statistical applications. Thereby a better
understanding of the problem being investigated is achieved, and subsequent analyses of
the data frequently yield more accurate results if irrelevant variables have been eliminated.
It is therefore obviously important to investigate aspects of variable selection for kernel
methods.
Chapter 2 of the thesis is an introduction to the main part presented in Chapters 3 to 6. In
Chapter 2 some general background material on kernel methods is firstly provided, along
with an introduction to variable selection. Empirical evidence is presented substantiating
the claim that variable selection is a worthwhile enterprise in kernel classification
problems. Several aspects which complicate variable selection in kernel methods are
discussed.
An important property of kernel methods is that the original data are effectively
transformed before a classification algorithm is applied to it. The space in which the
original data reside is called input space, while the transformed data occupy part of a
feature space. In Chapter 3 we investigate whether variable selection should be performed
in input space or rather in feature space. A new approach to selection, so-called feature-toinput
space selection, is also proposed. This approach has the attractive property of
combining information generated in feature space with easy interpretation in input space. An empirical study reveals that effective variable selection requires utilisation of at least
some information from feature space.
Having confirmed in Chapter 3 that variable selection should preferably be done in feature
space, the focus in Chapter 4 is on two classes of selecion criteria operating in feature
space: criteria which are independent of the specific kernel classification algorithm and
criteria which depend on this algorithm. In this regard we concentrate on two kernel
classifiers, viz. support vector machines and kernel Fisher discriminant analysis, both of
which are described in some detail in Chapter 4. The chapter closes with a simulation
study showing that two of the algorithm-independent criteria are very competitive with the
more sophisticated algorithm-dependent ones.
In Chapter 5 we incorporate a specific strategy for searching through the space of variable
subsets into our investigation. Evidence in the literature strongly suggests that backward
elimination is preferable to forward selection in this regard, and we therefore focus on
recursive feature elimination. Zero- and first-order forms of the new selection criteria
proposed earlier in the thesis are presented for use in recursive feature elimination and their
properties are investigated in a numerical study. It is found that some of the simpler zeroorder
criteria perform better than the more complicated first-order ones.
Up to the end of Chapter 5 it is assumed that the number of variables to select is known.
We do away with this restriction in Chapter 6 and propose a simple criterion which uses the
data to identify this number when a support vector machine is used. The proposed criterion
is investigated in a simulation study and compared to cross-validation, which can also be
used for this purpose. We find that the proposed criterion performs well.
The thesis concludes in Chapter 7 with a summary and several discussions for further
research.
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Assessing the influence of observations on the generalization performance of the kernel Fisher discriminant classifierLamont, Morné Michael Connell 12 1900 (has links)
Thesis (PhD (Statistics and Actuarial Science))—Stellenbosch University, 2008. / Kernel Fisher discriminant analysis (KFDA) is a kernel-based technique that can be used
to classify observations of unknown origin into predefined groups. Basically, KFDA can
be viewed as a non-linear extension of Fisher’s linear discriminant analysis (FLDA). In
this thesis we give a detailed explanation how FLDA is generalized to obtain KFDA. We
also discuss two methods that are related to KFDA. Our focus is on binary classification.
The influence of atypical cases in discriminant analysis has been investigated by many
researchers. In this thesis we investigate the influence of atypical cases on certain aspects
of KFDA. One important aspect of interest is the generalization performance of the KFD
classifier. Several other aspects are also investigated with the aim of developing criteria
that can be used to identify cases that are detrimental to the KFD generalization
performance. The investigation is done via a Monte Carlo simulation study.
The output of KFDA can also be used to obtain the posterior probabilities of belonging to
the two classes. In this thesis we discuss two approaches to estimate posterior
probabilities in KFDA. Two new KFD classifiers are also derived which use these
probabilities to classify observations, and their performance is compared to that of the
original KFD classifier.
The main objective of this thesis is to develop criteria which can be used to identify cases
that are detrimental to the KFD generalization performance. Nine such criteria are
proposed and their merit investigated in a Monte Carlo simulation study as well as on
real-world data sets.
Evaluating the criteria on a leave-one-out basis poses a computational challenge,
especially for large data sets. In this thesis we also propose using the smallest enclosing
hypersphere as a filter, to reduce the amount of computations. The effectiveness of the
filter is tested in a Monte Carlo simulation study as well as on real-world data sets.
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Binary classification trees : a comparison with popular classification methods in statistics using different softwareLamont, Morné Michael Connell 12 1900 (has links)
Thesis (MComm) -- Stellenbosch University, 2002. / ENGLISH ABSTRACT: Consider a data set with a categorical response variable and a set of explanatory
variables. The response variable can have two or more categories and the explanatory
variables can be numerical or categorical. This is a typical setup for a classification
analysis, where we want to model the response based on the explanatory variables.
Traditional statistical methods have been developed under certain assumptions
such as: the explanatory variables are numeric only and! or the data follow a multivariate
normal distribution. hl practice such assumptions are not always met. Different research
fields generate data that have a mixed structure (categorical and numeric) and researchers
are often interested using all these data in the analysis. hl recent years robust methods
such as classification trees have become the substitute for traditional statistical methods
when the above assumptions are violated. Classification trees are not only an effective
classification method, but offer many other advantages.
The aim of this thesis is to highlight the advantages of classification trees. hl the
chapters that follow, the theory of and further developments on classification trees are
discussed. This forms the foundation for the CART software which is discussed in
Chapter 5, as well as other software in which classification tree modeling is possible. We
will compare classification trees to parametric-, kernel- and k-nearest-neighbour
discriminant analyses. A neural network is also compared to classification trees and
finally we draw some conclusions on classification trees and its comparisons with other
methods. / AFRIKAANSE OPSOMMING: Beskou 'n datastel met 'n kategoriese respons veranderlike en 'n stel verklarende
veranderlikes. Die respons veranderlike kan twee of meer kategorieë hê en die
verklarende veranderlikes kan numeries of kategories wees. Hierdie is 'n tipiese opset vir
'n klassifikasie analise, waar ons die respons wil modelleer deur gebruik te maak van die
verklarende veranderlikes.
Tradisionele statistiese metodes is ontwikkelonder sekere aannames soos: die
verklarende veranderlikes is slegs numeries en! of dat die data 'n meerveranderlike
normaal verdeling het. In die praktyk word daar nie altyd voldoen aan hierdie aannames
nie. Verskillende navorsingsvelde genereer data wat 'n gemengde struktuur het
(kategories en numeries) en navorsers wil soms al hierdie data gebruik in die analise. In
die afgelope jare het robuuste metodes soos klassifikasie bome die alternatief geword vir
tradisionele statistiese metodes as daar nie aan bogenoemde aannames voldoen word nie.
Klassifikasie bome is nie net 'n effektiewe klassifikasie metode nie, maar bied baie meer
voordele.
Die doel van hierdie werkstuk is om die voordele van klassifikasie bome uit te
wys. In die hoofstukke wat volg word die teorie en verdere ontwikkelinge van
klassifikasie bome bespreek. Hierdie vorm die fondament vir die CART sagteware wat
bespreek word in Hoofstuk 5, asook ander sagteware waarin klassifikasie boom
modelering moontlik is. Ons sal klassifikasie bome vergelyk met parametriese-, "kernel"-
en "k-nearest-neighbour" diskriminant analise. 'n Neurale netwerk word ook vergelyk
met klassifikasie bome en ten slote word daar gevolgtrekkings gemaak oor klassifikasie
bome en hoe dit vergelyk met ander metodes.
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Financial ratios, discriminant analysis and the prediction of corporate financial distress in Hong KongChan, Ho-cheong., 陳浩昌. January 1985 (has links)
published_or_final_version / Management Studies / Master / Master of Business Administration
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Magnetite as an indicator mineral in till: a test using the Mount Polley porphyry Cu-Au deposit, British ColumbiaPisiak, Laura 23 December 2015 (has links)
In the Canadian Cordillera, Mesozoic calcalkaline and alkaline intrusive igneous rocks that are prospective for hosting porphyry Cu-Au mineralization may be overlain by thick glacial overburden. Previous studies have shown that magnetite from ore deposits has a unique trace element signature that differs from magnetite in common igneous or metamorphic rocks. This study investigated if the composition of ore-related magnetite in till could provide a unique exploration tool to locate porphyry deposits in glaciated terrain. Bulk till samples were collected over an area of ~700 km2 surrounding the Mount Polley porphyry Cu-Au deposit, south-central British Columbia. Twenty elements were measured by LA-ICP-MS in ~50 detrital magnetite grains in each of 20 till samples. Previously proposed discrimination diagrams are proven to be of limited use in correctly identifying ore-related magnetite. Therefore, linear discriminant analysis (LDA) was performed on a compiled dataset of magnetite compositions from various porphyry deposits and intrusive igneous rocks in order to rigorously redefine the chemical signature of hydrothermal magnetite from porphyry systems. Application of the LDA models to magnetite in till found that the dispersal of hydrothermal grains from Mount Polley is coincident with the deposit and the interpreted ice-flow history. Anomalous concentrations of hydrothermal magnetite grains in till are detected up to 2.5 km west-southwest and 4 km northwest of the deposit, indicating that magnetite has a strong potential to be an effective indicator in mineral exploration for porphyry systems. / Graduate
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The Effect of Certain Modifications to Mathematical Programming Models for the Two-Group Classification ProblemWanarat, Pradit 05 1900 (has links)
This research examines certain modifications of the mathematical programming models to improve their classificatory performance. These modifications involve the inclusion of second-order terms and secondary goals in mathematical programming models. A Monte Carlo simulation study is conducted to investigate the performance of two standard parametric models and various mathematical programming models, including the MSD (minimize sum of deviations) model, the MIP (mixed integer programming) model and the hybrid linear programming model.
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Role of Behavioral Finance in Portfolio Investment Decisions: Evidence from IndiaSubash, Rahul January 2012 (has links)
I Role of Behavioral Finance in Portfolio Investment Decisions: Evidence from India Abstract Extreme volatility has plagued financial markets worldwide since the 2008 Global Crisis. Investor sentiment has been one of the key determinants of market movements. In this context, studying the role played by emotions like fear, greed and anticipation, in shaping up investment decisions seemed important. Behavioral Finance is an evolving field that studies how psychological factors affect decision making under uncertainty. This thesis seeks to find the influence of certain identified behavioral finance concepts (or biases), namely, Overconfidence, Representativeness, Herding, Anchoring, Cognitive Dissonance, Regret Aversion, Gamblers' Fallacy, Mental Accounting, and Hindsight Bias, on the decision making process of individual investors in the Indian Stock Market. Primary data for analysis was gathered by distributing a structured questionnaire among investors who were categorized as (i) young, and (ii) experienced. Results obtained by analyzing a sample of 92 respondents, out of which 53 admitted to having suffered a loss of at least 30% because of the crisis, revealed that the degree of exposure to the biases separated the behavioral pattern of young and experienced investors. Gamblers' Fallacy, Anchoring and...
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