TY  - ADVS
AU  - Brazell,Jeff
TI  - The SoV probit: a probit model with structured covariance for similarity effects and source of volume calculations 
T2  - Bayesian analysis in marketing : a breakthrough in customer analytics,
PY  - 2010///
CY  - London
PB  - Henry Stewart Talks
KW  - Marketing
KW  - Probits
N1  - Animated audio-visual presentation with synchronized narration; Title from title frames; Contents: Source of volume and cannibalization -- Product and line optimization questions -- Quick definitions -- Red bus / blue bus example -- What is IID? and IIA? -- We know IIA/IID gets source-of-volume wrong -- When should we care about substitution patterns? -- Source of volume calculations -- Cannibalization = Preference -- How do you handle IIA in your choice models? -- Simulating preference shares: state-of-the-art -- What does proportional substitution look like? -- Industry survey: how do current methods do? -- The SoV Probit -- Why should I care about correlated errors? -- Why aren't we all using Probit models? -- A structured covariance Probit -- Distance metrics -- Choice experiment -- Model fit statistics -- Parameter estimates -- WTP comparison -- Representative correlation matrix -- Does it work?; Access restricted to subscribers
UR  - https://hstalks.com/bm/1606/
UR  - https://hstalks.com/bm/p/422/
ER  -