32,076 research outputs found
Empirical-likelihood-based confidence interval for the mean with a heavy-tailed distribution
Empirical-likelihood-based confidence intervals for a mean were introduced by
Owen [Biometrika 75 (1988) 237-249], where at least a finite second moment is
required. This excludes some important distributions, for example, those in the
domain of attraction of a stable law with index between 1 and 2. In this
article we use a method similar to Qin and Wong [Scand.
J. Statist. 23 (1996) 209-219] to derive an empirical-likelihood-based
confidence interval for the mean when the underlying distribution has heavy
tails. Our method can easily be extended to obtain a confidence interval for
any order of moment of a heavy-tailed distribution
Confidence regions for high quantiles of a heavy tailed distribution
Estimating high quantiles plays an important role in the context of risk
management. This involves extrapolation of an unknown distribution function. In
this paper we propose three methods, namely, the normal approximation method,
the likelihood ratio method and the data tilting method, to construct
confidence regions for high quantiles of a heavy tailed distribution. A
simulation study prefers the data tilting method.Comment: Published at http://dx.doi.org/10.1214/009053606000000416 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Empirical Likelihodd Methods for an AR(1) process with ARCH(1) errors
For an AR(1) process with ARCH(1) errors, we propose empirical likelihood tests for testing whether the sequence is strictly stationary but has infinite variance, or the sequence is an ARCH(1) sequence or the sequence is an iid sequence. Moreover, an empirical likelihood based confidence interval for the parameter in the AR part is proposed. All of these results do not require more than a finite second moment of the innovations. This includes the case of t-innovations for any degree of freedom larger than 2, which serves as a prominent model for real data
Do individual investors learn from their trading experience
This paper investigates whether individual investors adjust their stock trading according to their stock selection abilities, which can be inferred from their trading history. Fixed-effect panel regressions provide strong evidence that the ability to forecast future stock returns significantly affects investors’ trading activity: investors purchase more actively if they are more likely to have stock selection ability. Furthermore, trading experience – measured by the number of purchases, the number of different stocks purchased, and the variance of purchase dollar amounts – significantly helps improve investors’ portfolio performance. In addition, we find that learning behavior varies across investors, which corroborates the heterogeneity of individual investorsindividual investors, learning, rationality, trading
Rationale Management Challenges in Requirements Engineering
Rationale and rationale management have been playing an increasingly prominent role in software system development mainly due to the knowledge demand during system evaluation, maintenance, and evolution, especially for large and complex systems. The rationale management for requirements engineering, as a commencing and critical phase in software development life cycle, is still under-exploited. In this paper, we first survey briefly the state-of-the-art on rationale employment and applications in requirements engineering. Secondly, we identify the challenges in integrating rationale management in requirements engineering activities in order to promote further investigations and define a research agenda on rationale management in requirements engineering.
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