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... should be incorporated in demand systems, despite the fact that they are rejected insample, because they improve out-of-sample forecasts. Comparing forecasts between models is relatively straightforward when the forecasted variable is continuous. Typically, the model with lowest mean-squaredforecast ...
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... restrictions should be incorporated in demand systems, despite the fact that they are rejected insample, because they improve out-of-sample forecasts. Comparing forecasts between models is relatively straightforward when the forecasted variable is continuous. Typically, the model with lowest mean-sq ...
Latent class models for clustering: A comparison with K
Latent class models for clustering: A comparison with K

... In the marketing research field, LC clustering is sometimes referred to as latent discriminant analysis (Dillon & Mulani 1989) because of the similarity to the statistical methods used in discriminant analysis (DISC) as well as logistic regression (LR). However, an important difference is that in di ...
Clinical Trials – A Bayesian Approach
Clinical Trials – A Bayesian Approach

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Modeling Economic Choice under Radical Uncertainty: Machine

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MKUKUTA/PER Consultations 2007
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Modeling wine preferences by data mining
Modeling wine preferences by data mining

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The econophysics in the Euromillions lottery
The econophysics in the Euromillions lottery

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Linear discriminant analysis
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Analysis of Variance (ANOVA)
Analysis of Variance (ANOVA)

... Example 2: The EPA (Environmental Protection Agency) tests public bodies of water for the presence of coliform bacteria. Aside from being potentially harmful to people in its own right, this bacteria tend to proliferate in polluted water, making the presence of coliform bacteria a surrogate for polu ...
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Accuracy and Precision in Cranial Radiosurgery
Accuracy and Precision in Cranial Radiosurgery

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Empirical Study on Human Resources Development of Marketing
Empirical Study on Human Resources Development of Marketing

... central China, tests the model by applying reliability analysis, validity analysis, correlation analysis etc, and then validates the model. By comparing the competence model of marketing managers in central China with the competence model of salesman established by Spencer, it can be found that alth ...
Bayesian Mixed Membership Models for Soft Clustering and
Bayesian Mixed Membership Models for Soft Clustering and

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a MS Powerpoint version of "Cluster Analysis vs. Market
a MS Powerpoint version of "Cluster Analysis vs. Market

... • LCCA is a model-based approach: – Statistical model is postulated for the population from which the data sample is obtained – LC model do not rely on the traditional modeling assumptions (linearity, normality, homogeneity) – It is assumed that a mixture of underlying probability distributions gene ...
Cluster Analysis vs. Market Segmentation
Cluster Analysis vs. Market Segmentation

... • LCCA is a model-based approach: – Statistical model is postulated for the population from which the data sample is obtained – LC model do not rely on the traditional modeling assumptions (linearity, normality, homogeneity) – It is assumed that a mixture of underlying probability distributions gene ...
new products, quality changes, and welfare measures computed
new products, quality changes, and welfare measures computed

... present, in section 3, the empirical importance of the various assumptions. I show that if one wants to produce a price index that allows for new brands and quality changes, then, depending on which assumptions are chosen, the results range from a 35% increase in the real price of cereal over 5 year ...
Global Pasta Sauce Industry Situation and Prospects
Global Pasta Sauce Industry Situation and Prospects

... http://www.orbisresearch.com/reports/index/global-pasta-sauceindustry-situation-and-prospects-research-report . For the sake of making you deeply understand the Pasta Sauce industry and meeting you needs to the report contents, Global Pasta Sauce Industry Situation and Prospects Research report will ...
Introduction to management science and marketing
Introduction to management science and marketing

... that response to market stimuli tend to be highly non linear, to exhibit a threshold effect (i.e,, some minimum level of the stimulus is required for there to be any response at all), to have carry-over effect (e-g-s response to this period's promotion will occur in future periods), and to decay wit ...
Customer Clustering using RFM analysis
Customer Clustering using RFM analysis

... In the present paper it is shown that the knowledge of RFM scoring of active e-banking users can rank them according to the pyramid model. This result was highlighted by the use of 2 clustering methods. Therefore, the e-banking unit of a bank may easily identify the most important users-customers. T ...
Chapter 5 - The University of Texas at Dallas
Chapter 5 - The University of Texas at Dallas

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invited-talk - Department of Computer Science and Engineering

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Selection of in vitro assays linked to an in vivo outcome

covariation learning, quality expectation and product valuation
covariation learning, quality expectation and product valuation

... the nature of the error (homoscedastic vs. heteroscedastic) may not matter, as long as the overall level of uncertainty remains the same. However, the distinction between homoscedastic and heteroscedastic uncertainty may be crucial if covariation judgments are in fact influenced by local variations ...
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Sensitivity analysis

Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be apportioned to different sources of uncertainty in its inputs. A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty. Ideally, uncertainty and sensitivity analysis should be run in tandem.The process of recalculating outcomes under alternative assumptions to determine the impact of variable under analysis Sensitivity analysis can be useful for a range of purposes, including Testing the robustness of the results of a model or system in the presence of uncertainty. Increased understanding of the relationships between input and output variables in a system or model. Uncertainty reduction: identifying model inputs that cause significant uncertainty in the output and should therefore be the focus of attention if the robustness is to be increased (perhaps by further research). Searching for errors in the model (by encountering unexpected relationships between inputs and outputs). Model simplification – fixing model inputs that have no effect on the output, or identifying and removing redundant parts of the model structure. Enhancing communication from modelers to decision makers (e.g. by making recommendations more credible, understandable, compelling or persuasive). Finding regions in the space of input factors for which the model output is either maximum or minimum or meets some optimum criterion (see optimization and Monte Carlo filtering). In case of calibrating models with large number of parameters, a primary sensitivity test can ease the calibration stage by focusing on the sensitive parameters. Not knowing the sensitivity of parameters can result in time being uselessly spent on non-sensitive ones.Taking an example from economics, in any budgeting process there are always variables that are uncertain. Future tax rates, interest rates, inflation rates, headcount, operating expenses and other variables may not be known with great precision. Sensitivity analysis answers the question, ""if these variables deviate from expectations, what will the effect be (on the business, model, system, or whatever is being analyzed), and which variables are causing the largest deviations?""
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