891 resultados para DISCRIMINANT-ANALYSIS
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Thesis (Master's)--University of Washington, 2016-06
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Minimum/maximum autocorrelation factor (MAF) is a suitable algorithm for orthogonalization of a vector random field. Orthogonalization avoids the use of multivariate geostatistics during joint stochastic modeling of geological attributes. This manuscript demonstrates in a practical way that computation of MAF is the same as discriminant analysis of the nested structures. Mathematica software is used to illustrate MAF calculations from a linear model of coregionalization (LMC) model. The limitation of two nested structures in the LMC for MAF is also discussed and linked to the effects of anisotropy and support. The analysis elucidates the matrix properties behind the approach and clarifies relationships that may be useful for model-based approaches. (C) 2003 Elsevier Science Ltd. All rights reserved.
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The present study investigated neuropsychological and psychological factors associated with successful treatment outcome following a group intervention for individuals with acquired brain injury (ABI). Participants were classified into two groups (Clinically Improved and Not Improved) based upon the findings of a previous study (Ownsworth, McFarland, & Young, 2000a). A discriminant analysis was used to predict group membership on three outcome measures (Awareness and Strategy Behaviour indices of the Self-Regulation Skills Interview and the Psychosocial Dimension of the Sickness Impact Profile) between pre-assessment and post-assessment, and between pre-assessment and 6 months follow-up. Neuropsychological factors involved measures of executive functioning and psychological factors were assessed using measures of personality-related denial and coping-related denial. Overall, the results indicated that individuals with impaired executive functioning were most likely to be classified as Clinically Improved on measures of awareness, strategy behaviour and psychosocial functioning. Individuals who deny or minimise their ABI symptoms were less likely to improve their psychosocial functioning following the group intervention. Future research needs to evaluate interventions for enhancing self-regulation skills and improving psychosocial functioning for individuals who employ denial as a main strategy for coping following ABI.
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Recent molecular analyses indicate that many reef coral species belong to hybridizing species complexes or "syngameons." Such complexes consist of numerous genetically distinct-species or lineages, which periodically split and/or fuse as they extend through time. During splitting and fusion, morphologic intermediates form and species overlap. Here we focus on processes associated with lineage fusion, specifically introgressive hybridization, and the recognition of such hybridization in the fossil record. Our approach involves comparing patterns of ecologic and morphologic overlap in genetically characterized modern species with fossil representatives of the same or closely related species. We similarly consider the long-term consequences of past hybridization on the structure of modern-day species boundaries. Our study involves the species complex Montastraea annularis s.l. and is based in the Bahamas, where, unlike other Caribbean locations, two of the three members of the complex today are not genetically distinct. We measured and collected colonies along linear transects across Pleistocene reef terraces of last interglacial age (approximately 125 Ka) on the islands of San Salvador, Andros, and Great Inagua. We performed quantitative ecologic and morphologic analyses of the fossil data, and compared patterns of overlap among species with data from modern localities where species are and are not genetically distinct. Ecologic and morphologic analyses reveal "moderate" overlap (>10%, but statistically significant differences) and sometimes "high" overlap (no statistically significant differences) among Pleistocene growth forms (= "species"). Ecologic analyses show that three species (massive, column, organ-pipe) co-occurred. Although organ-pipes had higher abundances in patch reef environments, columnar and massive species exhibited broad, completely overlapping distributions and had abundances that were not related to reef environment. For morphometric analyses, we used multivariate discriminant analysis on landmark data and linear measurements. The results show that columnar species overlap "moderately" with organ-pipe and massive species. Comparisons with genetically characterized colonies from Panama show that the Pleistocene Bahamas species have intermediate morphologies, and that the observed "moderate" overlap differs from the morphologic separation among the three modern species. In contrast, massive and columnar species from the Pleistocene of the Dominican Republic comprise distinct morphologic clusters, similar to the modern species; organ-pipe species exhibit "low" overlap (
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Persistent intermittent headache is a common disorder and is often accompanied by neck aching or stiffness, which could infer a cervical contribution to headache. However, the incidence of cervicogenic headache is estimated to be 14-18% of all chronic headaches, highlighting the need for clear criterion of cervical musculoskeletal impairment to identify cervicogenic headache sufferers who may benefit from treatments such as manual therapy. This study examined the presence of cervical musculoskeletal impairment in 77 subjects, 27 with cervicogenic headache, 25 with migraine with aura and 25 control subjects. Assessments included a photographic measure of posture, range of movement, cervical manual examination, pressure pain thresholds, muscle length, performance in the cranio-cervical flexion test and cervical kinaesthetic sense. The results indicated that when compared to the migraine with aura and control groups who scored similarly in the tests, the cervicogenic headache group had less range of cervical flexion/extension (P = 0.048) and significantly higher incidences of painful upper cervical joint dysfunction assessed by manual examination (all P < 0.05) and muscle tightness (P < 0.05). Sternocleidomastoid normalized EMG values were higher in the latter three stages of the cranio-cervical flexion test although they failed to reach significance. There were no between group differences for other measures. A discriminant analysis revealed that manual examination could discriminate the cervicogenic headache group from the other subjects (migraine with aura and control subjects combined) with an 80% sensitivity. (C) 2005 Elsevier Ltd. All rights reserved.
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This trial of cognitive-behavioural therapy (CBT) based amphetamine abstinence program (n = 507) focused on refusal self-efficacy, improved coping, improved problem solving and planning for relapse prevention. Measures included the Severity of Dependence Scale (SDS), the General Health Questionnaire-28 (GHQ-28) and Amphetamine Refusal Self-Efficacy. Psychiatric case identification (caseness) across the four GHQ-28 sub-scales was compared with Australian normative data. Almost 90% were amphetamine-dependent (SDS 8.15 +/- 3.17). Pretreatment, all GHQ-28 sub-scale measures were below reported Australian population values. Caseness was substantially higher than Australian normative values {Somatic Symptoms (52.3%), Anxiety (68%), Social Dysfunction (46.5%) and Depression (33.7%). One hundred and sixty-eight subjects (33%) completed and reported program abstinence. Program completers reported improvement across all GHQ-28 sub-scales Somatic Symptoms (p < 0.001), Anxiety (p < 0.001), Social Dysfunction (p < 0.001) and Depression (p < 0.001)}. They also reported improvement in amphetamine refusal self-efficacy (p < 0.001). Improvement remained significant following intention-to-treat analyses, imputing baseline data for subjects that withdrew from the program. The GHQ-28 sub-scales, Amphetamine Refusal Self-Efficacy Questionnaire and the SDS successfully predicted treatment compliance through a discriminant analysis function (p
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Objective: To investigate whether the recently developed (statistically derived) "ASsessment in Ankylosing Spondylitis Working Group" improvement criteria (ASAS-IC) for ankylosing spondylitis (AS) reflect clinically relevant improvement according to the opinion of an expert panel. Methods: The ASAS-IC consist of four domains: physical function, spinal pain, patient global assessment, and inflammation. Scores on these four domains of 55 patients with AS, who had participated in a non-steroidal anti-inflammatory drug efficacy trial, were presented to an international expert panel (consisting of patients with AS and members of the ASAS Working Group) in a three round Delphi exercise. The number of (non-) responders according to the ASAS-IC was compared with the final-consensus of the experts. The most important domains in the opinion of the experts were identified, and also selected with discriminant analysis. A number of provisional criteria sets that best represented the consensus of the experts were defined. Using other datasets, these clinically derived criteria sets as well as the statistically derived ASAS-IC were then tested for discriminative properties and for agreement with the end of trial efficacy by patient and doctor. Results: Forty experts completed the three Delphi rounds. The experts considered twice as many patients to be responders than the ASAS-IC (42 v 21). Overall agreement between experts and ASAS-IC was 62%. Spinal pain was considered the most important domain by most experts and was also selected as such by discriminant analysis. Provisional criteria sets with an agreement of greater than or equal to 80% compared with the consensus of the experts showed high placebo response rates (27-42%), in contrast with the ASAS-IC with a predefined placebo response rate of 25%. All criteria sets and the ASAS-IC discriminated well between active and placebo treatment (chi(2) = 36-45; p < 0.001). Compared with the end of trial efficacy assessment, the provisional criteria sets showed an agreement of 71-82%, sensitivity of 67-83%, and specificity of 81-88%. The ASAS-IC showed an agreement of 70%, sensitivity of 62%, and specificity of 89%. Conclusion: The ASAS-IC are strict in defining response, are highly specific, and consequently show lower sensitivity than the clinically derived criteria sets. However, those patients who are considered as responders by applying the ASAS-IC are acknowledged as such by the expert panel as well as by. patients' and doctors' judgments, and are therefore likely to be true responders.
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This thesis describes the development of a complete data visualisation system for large tabular databases, such as those commonly found in a business environment. A state-of-the-art 'cyberspace cell' data visualisation technique was investigated and a powerful visualisation system using it was implemented. Although allowing databases to be explored and conclusions drawn, it had several drawbacks, the majority of which were due to the three-dimensional nature of the visualisation. A novel two-dimensional generic visualisation system, known as MADEN, was then developed and implemented, based upon a 2-D matrix of 'density plots'. MADEN allows an entire high-dimensional database to be visualised in one window, while permitting close analysis in 'enlargement' windows. Selections of records can be made and examined, and dependencies between fields can be investigated in detail. MADEN was used as a tool for investigating and assessing many data processing algorithms, firstly data-reducing (clustering) methods, then dimensionality-reducing techniques. These included a new 'directed' form of principal components analysis, several novel applications of artificial neural networks, and discriminant analysis techniques which illustrated how groups within a database can be separated. To illustrate the power of the system, MADEN was used to explore customer databases from two financial institutions, resulting in a number of discoveries which would be of interest to a marketing manager. Finally, the database of results from the 1992 UK Research Assessment Exercise was analysed. Using MADEN allowed both universities and disciplines to be graphically compared, and supplied some startling revelations, including empirical evidence of the 'Oxbridge factor'.
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The thesis began as a study of new firm formation. Preliminary research suggested that infant death rate was considered to be a closely related problem and the search was for a theory of new firm formation which would explain both. The thesis finds theories of exit and entry inadequate in this respect and focusses instead on theories of entrepreneurship, particularly those which concentrate on entrepreneurship as an agent of change. The role of information is found to be fundamental to economic change and an understanding of information generation and dissemination and the nature and direction of information flows is postulated to lead coterminously to an understanding of entrepreneurhsip and economic change. The economics of information is applied to theories of entrepreneurhsip and some testable hypotheses are derived. The testing relies on etablishing and measuring the information bases of the founders of new firms and then testing for certain hypothesised differences between the information bases of survivors and non-survivors. No theory of entrepreneurship is likely to be straightforwardly testable and many postulates have to be established to bring the theory to a testable stage. A questionnaire is used to gather information from a sample of firms taken from a new micro-data set established as part of the work of the thesis. Discriminant Analysis establishes the variables which best distinguish between survivors and non-survivors. The variables which emerge as important discriminators are consistent with the theory which the analysis is testing. While there are alternative interpretations of the important variables, collective consistency with the theory under test is established. The thesis concludes with an examination of the implications of the theory for policy towards stimulating new firm formation.
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Since the Second World War a range of policies have been implemented by central and local government agencies, with a view to improving accessibility to facilities, housing and employment opportunities within rural areas. It has been suggested that a lack of reasonable access to a range of such facilities and opportunities constitutes a key aspect of deprivation or disadvantage for rural residents. Despite considerable interest, very few attempts have been made to assess the nature and incidence of this disadvantage or the reaction of different sections of the population of rural areas to it. Moreover, almost all previous assessments have relied on so-called 'objective' measures of accessibility and disadvantage and failed to consider the relationship between such measures and 'subjective' measures such as individual perceptions. It is this gap in knowledge that the research described in this thesis has addressed. Following a critical review of relevant literature the thesis describes the way in which data on 'objective' and 'subjective' indicators of accessibility and behavioural responses to accessibility problems was collected, in six case study areas in Shropshire. Analysis of this data indicates that planning and other government policies have failed to significantly improve rural resident's accessibility to their basic requirements, and may in some cases have exacerbated it, and that as a result certain sections of the rural population are relatively disadvantaged. Moreover, analysis shows that .certain aspects of individual subjective' assessments of such accessibility disadvantage are significantly associated with more easily-obtained 'objective' measures. By using discriminant analysis the research demonstrates that it is possible to predict the likely levels of satisfaction with access to facilities from a range of 'objective' measures. The research concludes by highlighting the potential practical applications of such indicators in policy formulation, policy appraisal and policy evaluation.
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Decomposition of domestic wastes in an anaerobic environment results in the production of landfill gas. Public concern about landfill disposal and particularly the production of landfill gas has been heightened over the past decade. This has been due in large to the increased quantities of gas being generated as a result of modern disposal techniques, and also to their increasing effect on modern urban developments. In order to avert diasters, effective means of preventing gas migration are required. This, in turn requires accurate detection and monitoring of gas in the subsurface. Point sampling techniques have many drawbacks, and accurate measurement of gas is difficult. Some of the disadvantages of these techniques could be overcome by assessing the impact of gas on biological systems. This research explores the effects of landfill gas on plants, and hence on the spectral response of vegetation canopies. Examination of the landfill gas/vegetation relationship is covered, both by review of the literature and statistical analysis of field data. The work showed that, although vegetation health was related to landfill gas, it was not possible to define a simple correlation. In the landfill environment, contribution from other variables, such as soil characteristics, frequently confused the relationship. Two sites are investigated in detail, the sites contrasting in terms of the data available, site conditions, and the degree of damage to vegetation. Gas migration at the Panshanger site was dominantly upwards, affecting crops being grown on the landfill cap. The injury was expressed as an overall decline in plant health. Discriminant analysis was used to account for the variations in plant health, and hence the differences in spectral response of the crop canopy, using a combination of soil and gas variables. Damage to both woodland and crops at the Ware site was severe, and could be easily related to the presence of gas. Air photographs, aerial video, and airborne thematic mapper data were used to identify damage to vegetation, and relate this to soil type. The utility of different sensors for this type of application is assessed, and possible improvements that could lead to more widespread use are identified. The situations in which remote sensing data could be combined with ground survey are identified. In addition, a possible methodology for integrating the two approaches is suggested.
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With business incubators deemed as a potent infrastructural element for entrepreneurship development, business incubation management practice and performance have received widespread attention. However, despite this surge of interest, scholars have questioned the extent to which business incubation delivers added value. Thus, there is a growing awareness among researchers, practitioners and policy makers of the need for more rigorous evaluation of the business incubation output performance. Aligned to this is an increasing demand for benchmarking business incubation input/process performance and highlighting best practice. This paper offers a business incubation assessment framework, which considers input/process and output performance domains with relevant indicators. This tool adds value on different levels. It has been developed in collaboration with practitioners and industry experts and therefore it would be relevant and useful to business incubation managers. Once a large enough database of completed questionnaires has been populated on an online platform managed by a coordinating mechanism, such as a business incubation membership association, business incubator managers can reflect on their practices by using this assessment framework to learn their relative position vis-à-vis their peers against each domain. This will enable them to align with best practice in this field. Beyond implications for business incubation management practice, this performance assessment framework would also be useful to researchers and policy makers concerned with business incubation management practice and impact. Future large-scale research could test for construct validity and reliability. Also, discriminant analysis could help link input and process indicators with output measures.
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2002 Mathematics Subject Classification: 62P10.
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It is well established that accent recognition can be as accurate as up to 95% when the signals are noise-free, using feature extraction techniques such as mel-frequency cepstral coefficients and binary classifiers such as discriminant analysis, support vector machine and k-nearest neighbors. In this paper, we demonstrate that the predictive performance can be reduced by as much as 15% when the signals are noisy. Specifically, in this paper we perturb the signals with different levels of white noise, and as the noise become stronger, the out-of-sample predictive performance deteriorates from 95% to 80%, although the in-sample prediction gives overly-optimistic results. ACM Computing Classification System (1998): C.3, C.5.1, H.1.2, H.2.4., G.3.
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Background: Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences.Results: A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity.Conclusions: AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin. © 2013 Dimitrov et al.; licensee BioMed Central Ltd.