Antecedents and consequences of apparel involvement
... However, little work has focused thoroughly on apparel involvement. There were
several studies that used the concept of apparel involvement in relation to other consumer
behavior variables. However, these studies simply borrowed previous perspectives and
measures by merely attaching the concept of p ...
Real business cycles: A Reader
... G.King and Charles I.Plosser for “Real Business Cycles and the Test of
the Adelmans” in Journal of Monetary Economics, vol. 33:2, April 1989, pp.
405–438. Louisiana State University Press and James E.Hartley, Kevin
D.Salyer and Steven M.Sheffrin for “Calibration and Real Business Cycle
Models: An Un ...
... • How are different transport modes affected? Are existing public
transport systems able to step into the breach? If not, which
investments are necessary to prepare public transport for possible
• Which new opportunities and challenges will arise for electric
mobility as well as neig ...
New Measures of Clumpiness for Incidence Data Yao Zhang Eric T. Bradlow
... interest and never come back, or just simply have become “inactive” for a period of time and
return. Once he returns, it is very likely that he would make a lot of purchases again. As a
result, a nice/valid measure of clumpiness adds a new building block to profiling customers by
which companies ar ...
Learning Latent Sentiment Scopes for Entity
... complete sentence, while they do not overlap with one another.
However, the sentiment scopes are not explicitly annotated in the training data. We thus need to build models
that can automatically learn such latent information from the
data. As we will see later, with the above assumptions on the
A Joint Model of Usage and Churn in Contractual
... (e.g., work travel means fewer visits to the gym in a given week), it is easy to think of a common
factor (e.g., commitment) affecting both decisions. As a consequence, if we want to understand
and predict customer usage and renewal behaviors, we should model them jointly.
Another common characteri ...
... over 95% had eaten these nuts in the previous
twelve months. The number of pecan purchases during the previous six months for each
respondent is used as the dependent variable
in the model. Pecan purchasers averaged approximately three purchases during the survey
period. We label respondents who did ...
... extended this notion of hierarchy (temporal sequence) by
shifting focus to a set of core constructs: attention, affect,
memory, and desirability. These core constructs can affect
advertising success independently or in combination (Haley
and Baldinger 2000; Morwitz, Steckel, and Gupta 2007;
Walker a ...
Bank of England working paper no.58
... utility of consumption. Equation (4) determines how consumers trade
off consumption and leisure and states that the marginal rate of
substitution between them equals the real wage rate. Under standard
assumptions on the utility function (4) suggests that increases in the
real wage should bring forth ...
Some Applications of Data Mining Tools in Database Marketing
... CART analysis, when the misclassification rate for
Logit regression is a parametric appr"ach which is
well understood and widely used in csregorical
EC is 18.38%, the rate is about 26% for non-EC.
When the misclassification rate for EC is 11.6%,
data analysis for its simplicity and be:rer
Predicting Advertising Success Beyond Traditional Measures: New
... equity. Because popular brands tend to have higher premea
sure scores, it is important to account for this bias before
making any judgments about shifts in desirability. Walker
and Dubitsky (1994) propose a method by which change
scores are normalized by using a baseline predicted average
result (P ...
chapter one - Covenant University Repository
... structurally heavily parameterized and essentially static. In addition, they cannot handle
uncertainties and intertemporal features. This thesis will contribute to quantitative
macroeconomic assessment of the Nigerian economy based on the characterization and
analyses of business cycles in a Dynamic ...
Analyses of Online Advertising Performance Using
... 2. Categorize digital marketing channels based on the analysis.
3. Propose an attribution modeling approach and key metrics that best fit the industry
based on the given datasets.
Due to the nature of the problem, a set of hypotheses, which will be reviewed during
the analysis, must be set to suppor ...
... distance measures. Compare the results across measures
to determine the stability of the solutions.
2. Use different methods of clustering and compare the results.
3. Split the data randomly into halves. Perform clustering
separately on each half. Compare cluster centroids across
the two subsamples. ...
Asymmetric preference formation in willingness to pay estimates in
... random taste heterogeneity. This shows that by accounting for APS, modellers can
reduce the impact of the unobserved part of utility on model results.
In this paper, we look at a different issue that falls within the general field of attribute
processing strategies, namely whether there are asymmetr ...
Elements of a Decision Analysis
... Bidding for a Government Contract at SciTools
• The value model in this example is straightforward but in
other examples it is often complex
– If SciTools decides right now not to bid, then its monetary
values is $0 - no gain, no loss
– If they make a bid and are underbid by a competitor, then
APPLICATION OF NIR SPECTROSCOPY FOR WHISKY
... figure 1B [Inon et al. 2006]. The spectral range between 6000-5660 cm-1
corresponds to the first overtone of the C-H stretching vibrations in CH3
and CH2 group. The spectral range between 4580-4200 cm-1 corresponds
to the combination of CH vibrations [Workman 1996].
NIR spectra of the same types of ...
... than variance shared between the LV's. If this is true for the target LV and all the other LV's, this
suggests the discriminant validity of the target LV.
Unfortunately, experience suggests that AVE in LV Interactions and Quadratics is typically low,
frequently less than 50%. For example, while the ...
Selecting Peer Institutions with Cluster Analysis
... The appropriate clustering algorithm and parameter settings (including values such as the distance
function, density threshold or the number of expected clusters) depend on the individual data set and
intended use of the results. Cluster analysis is an iterative process of knowledge discovery and
estimating latent variable interactions and quadratics
... This technique is popular in the substantive literatures, and is a preferred technique in some
situations. Jaccard, Turissi and Wan (1990) state that subgroup analysis may be appropriate when the
model could be structurally different for different subgroups of subjects. They also point out that an
Incorporating Competitor Data into CRM
... public-policy motivated findings suggest detailing and sampling serve primarily an
Using a fixed-effects model, physician-specific effects are considered by Mizik
and Jacobson (2004). The authors also include lagged prescriptions to allow for physician
preferences to persist over t ...
Estimating Supply Elasticity for Disaggregated Paper Products: A
... regression that estimates, in the present case, how market
price changes are influenced by demand. To identify these
effects, economic variables that are highly correlated with
product demand are regressed against output price. Predicted price values from this first-stage regression are then
used in ...
Assessment of the precision of an analytical technique
... total variance by hand (using Excel): this will be helpful to understand the relationship
between these variances and the different "Mean Squares" given by Excel i.e. the
Between Mean Square (BMS) (“entre groupes” in French ) and the Within Mean
Square (WMS) (“à l'intérieur du groupe” in French).
... because an individual’s characteristics are invariant among a set of choices. In an
econometric sense this means that the effect of individual characteristics are not
identifiable in the probability of choosing commodities. In essence, the model parameters
are the same for each sampled individual im ...
Download paper (PDF)
... stream reflects not only the importance of new products, but also the role of diffusion research in helping managers to
better plan their entry strategy, target the right consumer, and anticipate demand so as to formulate an efficient and
effective promotion, production, and distribution strategy.
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?""