The Experts below are selected from a list of 30 Experts worldwide ranked by ideXlab platform
Thomas Otter - One of the best experts on this subject based on the ideXlab platform.
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Successive Sample selection and its relevance for management decisions
Marketing Science, 2013Co-Authors: Stephan Wachtel, Thomas OtterAbstract:We reanalyze endogenous Sample selection in the context of customer scoring, targeting, and influencing decisions. Scoring relies on ordered lists of probabilities that customers act in a way that contributes revenues, e.g., purchase something from the firm. Targeting identifies constrained sets of covariate patterns associated with high probabilities of these acts. Influencing aims at changing the probabilities that individual customers act accordingly through marketing activities. We show that successful targeting and influencing decisions require inference that controls for endogenous selection, whereas scoring can proceed relatively successfully based on simpler models that provide local approximations, capitalizing on spurious effects of observed covariates. To facilitate the type of inference required for targeting and influencing, we develop a prior that frees the analyst from having to specify often arbitrary exclusion restrictions for model identification a priori or to explicitly compare all possible models. We cover exclusions of observed as well as unobserved covariates that may cause the Successive selections to be dependent. We automatically infer the dependence structure among selection stages using Markov chain Monte Carlo-based variable selection, before identifying the scale of latent variables. The adaptive parsimony achieved through our prior is particularly helpful in applications where the number of Successive selections exceeds two, a relevant but underresearched situation.
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Successive Sample selection and its relevance for management decisions
Social Science Research Network, 2012Co-Authors: Stephan Wachtel, Thomas OtterAbstract:We re-analyze endogenous Sample selection in the context of customer scoring, targeting, and influencing decisions. Scoring relies on ordered lists of probabilities that customers act in a way that contributes revenues, e.g. purchase something from the firm. Targeting identifies constrained sets of covariate patterns associated with high probabilities of these acts. And influencing aims at changing the probabilities that individual customers act accordingly through marketing activities. We show that successful targeting and influencing decisions require inference that controls for endogenous selection, whereas scoring can proceed relatively successfully based on simpler models that provide (local) approximations, capitalizing on spurious effects of observed covariates. To facilitate the type of inference required for targeting and influencing, we develop a prior that frees the analyst from having to specify (often arbitrary) exclusion restrictions for model identification a priori, or explicitly compare all possible models. We cover both exclusions of observed as well as unobserved covariates that may cause the Successive selections to be dependent. We automatically infer the dependence structure among selection stages using MCMC based variable selection, before identifying the scale of latent variables. The adaptive parsimony achieved through our prior is particularly helpful in applications where the number of Successive selections exceeds two, a relevant but under-researched situation.
Stephan Wachtel - One of the best experts on this subject based on the ideXlab platform.
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Successive Sample selection and its relevance for management decisions
Marketing Science, 2013Co-Authors: Stephan Wachtel, Thomas OtterAbstract:We reanalyze endogenous Sample selection in the context of customer scoring, targeting, and influencing decisions. Scoring relies on ordered lists of probabilities that customers act in a way that contributes revenues, e.g., purchase something from the firm. Targeting identifies constrained sets of covariate patterns associated with high probabilities of these acts. Influencing aims at changing the probabilities that individual customers act accordingly through marketing activities. We show that successful targeting and influencing decisions require inference that controls for endogenous selection, whereas scoring can proceed relatively successfully based on simpler models that provide local approximations, capitalizing on spurious effects of observed covariates. To facilitate the type of inference required for targeting and influencing, we develop a prior that frees the analyst from having to specify often arbitrary exclusion restrictions for model identification a priori or to explicitly compare all possible models. We cover exclusions of observed as well as unobserved covariates that may cause the Successive selections to be dependent. We automatically infer the dependence structure among selection stages using Markov chain Monte Carlo-based variable selection, before identifying the scale of latent variables. The adaptive parsimony achieved through our prior is particularly helpful in applications where the number of Successive selections exceeds two, a relevant but underresearched situation.
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Successive Sample selection and its relevance for management decisions
Social Science Research Network, 2012Co-Authors: Stephan Wachtel, Thomas OtterAbstract:We re-analyze endogenous Sample selection in the context of customer scoring, targeting, and influencing decisions. Scoring relies on ordered lists of probabilities that customers act in a way that contributes revenues, e.g. purchase something from the firm. Targeting identifies constrained sets of covariate patterns associated with high probabilities of these acts. And influencing aims at changing the probabilities that individual customers act accordingly through marketing activities. We show that successful targeting and influencing decisions require inference that controls for endogenous selection, whereas scoring can proceed relatively successfully based on simpler models that provide (local) approximations, capitalizing on spurious effects of observed covariates. To facilitate the type of inference required for targeting and influencing, we develop a prior that frees the analyst from having to specify (often arbitrary) exclusion restrictions for model identification a priori, or explicitly compare all possible models. We cover both exclusions of observed as well as unobserved covariates that may cause the Successive selections to be dependent. We automatically infer the dependence structure among selection stages using MCMC based variable selection, before identifying the scale of latent variables. The adaptive parsimony achieved through our prior is particularly helpful in applications where the number of Successive selections exceeds two, a relevant but under-researched situation.
Richard G M Morris - One of the best experts on this subject based on the ideXlab platform.
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glutamate receptor mediated encoding and retrieval of paired associate learning
Nature, 2003Co-Authors: Rosamund F Langston, Richard G M MorrisAbstract:Paired-associate learning is often used to examine episodic memory in humans1. Animal models include the recall of food-cache locations by scrub jays2 and sequential memory3,4. Here we report a model in which rats encode, during Successive Sample trials, two paired associates (flavours of food and their spatial locations) and display better-than-chance recall of one item when cued by the other. In a first study, pairings of a particular foodstuff and its location were never repeated, so ensuring unique ‘what–where’ attributes. Blocking N-methyl-d-aspartate receptors in the hippocampus—crucial for the induction of certain forms of activity-dependent synaptic plasticity5,6—impaired memory encoding but had no effect on recall. Inactivating hippocampal neural activity by blocking α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptors impaired both encoding and recall. In a second study, two paired associates were trained repeatedly over 8 weeks in new pairs, but blocking of hippocampal AMPA receptors did not affect their recall. Thus we conclude that unique what–where paired associates depend on encoding and retrieval within a hippocampal memory space7,8, with consolidation of the memory traces representing repeated paired associates in circuits elsewhere.
Thomas H. Wright - One of the best experts on this subject based on the ideXlab platform.
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Properties of Successive Sample moment estimators
2011Co-Authors: Eytan Barouch, Simeon Chow, Gordon M. Kaufman, Thomas H. WrightAbstract:Moment type estimation of characteristics of a Successively Sampled finite population requires finding a solution to a pair of transcendental equations. Some global properties of two structurally distinct pairs—a symmetric and an asymmetric pair—of such equations are presented. Results of a Monte Carlo experiment designed to compare the performance of estimators computed by solving these transcendental equation pairs are reported.
Karl Heinz Hohne - One of the best experts on this subject based on the ideXlab platform.
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high quality rendering of attributed volume data
IEEE Visualization, 1998Co-Authors: Ulf Tiede, Thomas Schiemann, Karl Heinz HohneAbstract:For high quality rendering of objects segmented from tomographic volume data the precise location of the boundaries of adjacent objects in subvoxel resolution is required. We describe a new method that determines the membership of a given Sample point to an object by reclassifying the Sample point using interpolation of the original intensity values and searching for the best fitting object in the neighbourhood. Using a ray-casting approach we then compute the surface location between Successive Sample points along the viewing-ray by interpolation or bisection. The accurate calculation of the object boundary enables a much more precise computation of the gray-level-gradient yielding the surface normal. Our new approach significantly improves the quality of reconstructed and shaded surfaces and reduces aliasing artifacts for animations and magnified views. We illustrate the results on different cases including the Visible-Human-Data, where we achieve nearly photo-realistic images.
