3,469 research outputs found
Does consistency predict accuracy of beliefs?: Economists surveyed about PSA
Subjective beliefs and behavior regarding the Prostate Specific Antigen (PSA) test for prostate cancer were surveyed among attendees of the 2006 meeting of the American Economic Association. Logical inconsistency was measured in percentage deviations from a restriction imposed by Bayes’ Rule on pairs of conditional beliefs. Economists with inconsistent beliefs tended to be more accurate than average, and consistent Bayesians were substantially less accurate. Within a loss function framework, we look for and cannot find evidence that inconsistent beliefs cause economic losses. Subjective beliefs about cancer risks do not predict PSA testing decisions, but social influences do.logical consistency, predictive accuracy, elicitation, non-Bayesian, ecological rationality
Does Consistency Predict Accuracy of Beliefs?: Economists Surveyed About PSA
Subjective beliefs and behavior regarding the Prostate Specific Antigen (PSA) test for prostate cancer were surveyed among attendees of the 2006 meeting of the American Economic Association. Logical inconsistency was measured in percentage deviations from a restriction imposed by Bayes’ Rule on pairs of conditional beliefs. Economists with inconsistent beliefs tended to be more accurate than average, and consistent Bayesians were substantially less accurate. Within a loss function framework, we look for and cannot find evidence that inconsistent beliefs cause economic losses. Subjective beliefs about cancer risks do not predict PSA testing decisions, but social influences do.logical consistency, predictive accuracy, elicitation, non-Bayesian, ecological rationality
Defining Interestigness for Association Rules
Interestingness in Association Rules has been a major topic of research in the past decade. The
reason is that the strength of association rules, i.e. its ability to discover ALL patterns given some thresholds
on support and confidence, is also its weakness. Indeed, a typical association rules analysis on real data often
results in hundreds or thousands of patterns creating a data mining problem of the second order. In other
words, it is not straightforward to determine which of those rules are interesting for the end-user. This paper
provides an overview of some existing measures of interestingness and we will comment on their properties.
In general, interestingness measures can be divided into objective and subjective measures. Objective
measures tend to express interestingness by means of statistical or mathematical criteria, whereas subjective
measures of interestingness aim at capturing more practical criteria that should be taken into account, such as
unexpectedness or actionability of rules. This paper only focusses on objective measures of interestingness
A Model-Based Frequency Constraint for Mining Associations from Transaction Data
Mining frequent itemsets is a popular method for finding associated items in
databases. For this method, support, the co-occurrence frequency of the items
which form an association, is used as the primary indicator of the
associations's significance. A single user-specified support threshold is used
to decided if associations should be further investigated. Support has some
known problems with rare items, favors shorter itemsets and sometimes produces
misleading associations.
In this paper we develop a novel model-based frequency constraint as an
alternative to a single, user-specified minimum support. The constraint
utilizes knowledge of the process generating transaction data by applying a
simple stochastic mixture model (the NB model) which allows for transaction
data's typically highly skewed item frequency distribution. A user-specified
precision threshold is used together with the model to find local frequency
thresholds for groups of itemsets. Based on the constraint we develop the
notion of NB-frequent itemsets and adapt a mining algorithm to find all
NB-frequent itemsets in a database. In experiments with publicly available
transaction databases we show that the new constraint provides improvements
over a single minimum support threshold and that the precision threshold is
more robust and easier to set and interpret by the user
An Answer to Multiple Problems with Analysis of Data on Harms?
Discussion of "Multivariate Bayesian Logistic Regression for Analysis of
Clinical Trial Safety Issues" by W. DuMouchel [arXiv:1210.0385].Comment: Published in at http://dx.doi.org/10.1214/12-STS381C the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Developmental Level of Moral Judgment Influences Behavioral Patterns during Moral Decision-making
We developed and tested a behavioral version of the Defining Issues Test-1 revised (DIT-1r), which is a measure of the development of moral judgment. We conducted a behavioral experiment using the behavioral Defining Issues Test (bDIT) to examine the relationship between participants’ moral developmental status, moral competence, and reaction time when making moral judgments. We found that when the judgments were made based on the preferred moral schema, the reaction time for moral judgments was significantly moderated by the moral developmental status. In addition, as a participant becomes more confident with moral judgment, the participant differentiates the preferred versus other schemas better particularly when the participant’s abilities for moral judgment are more developed
Implications of probabilistic data modeling for rule mining
Mining association rules is an important technique for discovering meaningful patterns in transaction databases. In the current literature, the properties of algorithms to mine associations are discussed in great detail. In this paper we investigate properties of transaction data sets from a probabilistic point of view. We present a simple probabilistic framework for transaction data and its implementation using the R statistical computing environment. The framework can be used to simulate transaction data when no associations are present. We use such data to explore the ability to filter noise of confidence and lift, two popular interest measures used for rule mining. Based on the framework we develop the measure hyperlift and we compare this new measure to lift using simulated data and a real-world grocery database.Series: Research Report Series / Department of Statistics and Mathematic
An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets
As advances in technology allow for the collection, storage, and analysis of
vast amounts of data, the task of screening and assessing the significance of
discovered patterns is becoming a major challenge in data mining applications.
In this work, we address significance in the context of frequent itemset
mining. Specifically, we develop a novel methodology to identify a meaningful
support threshold s* for a dataset, such that the number of itemsets with
support at least s* represents a substantial deviation from what would be
expected in a random dataset with the same number of transactions and the same
individual item frequencies. These itemsets can then be flagged as
statistically significant with a small false discovery rate. We present
extensive experimental results to substantiate the effectiveness of our
methodology.Comment: A preliminary version of this work was presented in ACM PODS 2009. 20
pages, 0 figure
Cross-sectional and longitudinal associations of neighborhood cohesion and stressors with depressive symptoms in the Multiethnic Study of Atherosclerosis
Purpose: This study examined associations of neighborhood social cohesion, violence, and aesthetic quality with depressive symptoms among 2,619 healthy adults aged 45–84 years enrolled in the Multiethnic Study of Atherosclerosis. Methods: Neighborhood characteristics were estimated by surveying a separate sample of area residents. Measures of aesthetic environment, social cohesion, and violence were combined into a summary score with increasing scores indicating more favorable environments. Depressive symptoms were measured using the Center for Epidemiologic Studies-Depression (CES-D) scale. Marginal maximum likelihood estimation was used to assess associations of neighborhood characteristics with CES-D score at baseline and with the odds of developing incident depression (CES-D score =16 or use of antidepressants) over a 4–5 year follow-up among persons with CES-D less than 16 at baseline. Models were adjusted for age, income, education, and race/ethnicity. Results: Lower levels of social cohesion and aesthetic quality and higher levels of violence were associated with higher mean CES-D scores in men and women (P for trend <0.01, adjusted mean difference in CES-D per 1 SD increase in summary score -1.01 [95% confidence interval = -1.85, -0.17] and -1.08 [95% confidence interval = -1.88, -0.28] in men and women, respectively). Associations of neighborhood characteristics with incident depression were in the expected direction for women but confidence intervals were wide (odds ratio of incident depression = 0.89 [0.63, 1.26]). No association was seen for men (odds ratio = 0.96 [0.74, 1.25]). Conclusion: Neighborhood social cohesion, aesthetic quality, and violence are associated with the presence of depressive symptoms in residents
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