31,324 research outputs found
Have Econometric Analyses of Happiness Data Been Futile? A Simple Truth About Happiness Scales
Econometric analyses in the happiness literature typically use subjective
well-being (SWB) data to compare the mean of observed or latent happiness
across samples. Recent critiques show that comparing the mean of ordinal data
is only valid under strong assumptions that are usually rejected by SWB data.
This leads to an open question whether much of the empirical studies in the
economics of happiness literature have been futile. In order to salvage some of
the prior results and avoid future issues, we suggest regression analysis of
SWB (and other ordinal data) should focus on the median rather than the mean.
Median comparisons using parametric models such as the ordered probit and logit
can be readily carried out using familiar statistical softwares like STATA. We
also show a previously assumed impractical task of estimating a semiparametric
median ordered-response model is also possible by using a novel constrained
mixed integer optimization technique. We use GSS data to show the famous
Easterlin Paradox from the happiness literature holds for the US independent of
any parametric assumption
Measuring association via lack of co-monotonicity: the LOC index and a problem of educational assessment
Measuring association, or the lack of it, between variables plays an
important role in a variety of research areas, including education, which is of
our primary interest in this paper. Given, for example, student marks on
several study subjects, we may for a number of reasons be interested in
measuring the lack of co-monotonicity (LOC) between the marks, which rarely
follow monotone, let alone linear, patterns. For this purpose, in this paper we
explore a novel approach based on a LOC index, which is related to, yet
substantially different from, Eckhard Liebscher's recently suggested
coefficient of monotonically increasing dependence. To illustrate the new
technique, we analyze a data-set of student marks on mathematics, reading and
spelling
Stochastic Ordering under Conditional Modelling of Extreme Values: Drug-Induced Liver Injury
Drug-induced liver injury (DILI) is a major public health issue and of
serious concern for the pharmaceutical industry. Early detection of signs of a
drug's potential for DILI is vital for pharmaceutical companies' evaluation of
new drugs. A combination of extreme values of liver specific variables indicate
potential DILI (Hy's Law). We estimate the probability of severe DILI using the
Heffernan and Tawn (2004) conditional dependence model which arises naturally
in applications where a multidimensional random variable is extreme in at least
one component. We extend the current model by including the assumption of
stochastically ordered survival curves for different doses in a Phase 3 study.Comment: 24 pages, 5 figure
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