maximum principle for survey data analysis
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Maximum Principle for Survey Data Analysis. realiability tuning parameter the principle theoretical aspects practical recommendations illustration. Food preferences investigation. Food preferences investigation. Some theoretical aspects. - PowerPoint PPT PresentationTRANSCRIPT
Maximum Principle for Survey Data Analysis
• realiabilityrealiability• tuning parametertuning parameter• the principlethe principle• theoretical aspectstheoretical aspects• practical recommendationspractical recommendations• illustrationillustration
Food preferences investigationFood preferences investigation
TableTable 1 1 DairyDairy GrainGrain GreeneryGreenery FishFish MeatMeat TotalTotal
Respond. nr.1Respond. nr.1 XX XX 22
Respond. nr.2Respond. nr.2 XX XX XX XX 44
Respond. nr.3Respond. nr.3 XX XX 22
Respond. nr.4Respond. nr.4 XX XX XX XX 44
Respond. nr.5Respond. nr.5 XX XX 22
Respond nr. 6Respond nr. 6 XX XX XX XX XX 55
Respond. nr.7Respond. nr.7 XX XX 22
TotalTotal 33 55 55 55 33 2121
%% 43%43% 71%71% 71%71% 71%71% 43%43%
Food Food preferencespreferences investigation investigation
TableTable 2 2 DairyDairy GrainGrain GreeneryGreenery FishFish MeatMeat TotalTotal
Respond. nr.2Respond. nr.2 XX XX XX XX 44
Respond. nr.4Respond. nr.4 XX XX XX XX 44
Respond nr. 6Respond nr. 6 XX XX XX XX XX 55
TotalTotal 33 33 11 33 33 1313
TableTable 3 3 DairyDairy GrainGrain FishFish MeatMeat TotalTotal
Respond. nr.2Respond. nr.2 XX XX XX XX 44
Respond. nr.4Respond. nr.4 XX XX XX XX 44
Respond nr. 6Respond nr. 6 XX XX XX XX 44
TotalTotal 33 33 33 33 1313
Some theoretical aspectsSome theoretical aspects For some time I'm working on a problem of sampling a set of KFor some time I'm working on a problem of sampling a set of Kobservations (cases) from a large data set with N >> K cases so thatobservations (cases) from a large data set with N >> K cases so thatthe selected observations are as "different as possible". In morethe selected observations are as "different as possible". In moremathematical terms, I'm interested in locating those K cases whichmathematical terms, I'm interested in locating those K cases whichwill result in a (not necessarily Euclidean) distance matrix in whichwill result in a (not necessarily Euclidean) distance matrix in whichthe smallest off-diagonal entry d_ij is as large as possible.the smallest off-diagonal entry d_ij is as large as possible. I have developed an algorithm which seems to work very well and I have developed an algorithm which seems to work very well andgenerates sets which are either optimal or close to optimality withoutgenerates sets which are either optimal or close to optimality withoutcomputing the entire distance matrix. However, I'm thinking morecomputing the entire distance matrix. However, I'm thinking moreand more that this maybe a known problem to people who work inand more that this maybe a known problem to people who work inCluster Analysis, MDS, or classification. I wonder if anybody onCluster Analysis, MDS, or classification. I wonder if anybody onthis list could point me to some references about this searchthis list could point me to some references about this searchproblem.problem.
Thanks, Wolfgang HartmannThanks, Wolfgang Hartmann
Some theoretical aspectsSome theoretical aspects
In order to find a reliable component In order to find a reliable component K,K, it seems tautological that following it seems tautological that following our maximum principle, we have to solve maximization problem:our maximum principle, we have to solve maximization problem:
K = argmaxK = argmax(x,y)(x,y)FFkk(H)(H)
Positive / NegativePositive / Negative
Indifferent
Excellent Very good Bad Very bad
00 100100
Positive / NegativePositive / Negative
Negative/Positive Scale in the Questionnaire Negative/Positive Scale in the Questionnaire
Negative/Positive Scale in the QuestionnaireNegative/Positive Scale in the Questionnaire
Negative/Positive Scale in the QuestionnaireNegative/Positive Scale in the Questionnaire
Negative/Positive Scale in the QuestionnaireNegative/Positive Scale in the Questionnaire
Respondent 00 A0100038 actualRespondent 00 A0100038 actual
Respondent 00 A0100038 prognosesRespondent 00 A0100038 prognoses
Daglig stress - belastningsreaktioner
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Daglig stress - socialt netværk
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Lavere daglig stress - middel punkt
A/B type menneskets adfærd
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Salg
Kostvaner - Motionsvaner - Alkhogol - Kulilte
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Undersøgelse nr. 1
Undersøgelse nr. 2
0° 2° 4° 6° 8°
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P(t)
Temperature
Virksomhed A
t = 2,7°
Sundheds-feber for virksomhed ASundheds-feber for virksomhed A
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P(t)
Temperature
Virksomhed B
26,4°
Sundheds-feber for virksomhed BSundheds-feber for virksomhed B