23,613 research outputs found
Thermoluminescence and optically stimulated luminescence of gamma-irradiated mineral zircon
Thermoluminescence (TL) manifested by gamma-irradiated mineral zircon has shown a strong TL peak at about 165 °C which is due to recombination of electrons and Dy3+ related shallow hole traps. After they have been removed by a short preheat we have observed two TL peaks at 300-320 °C and â420 °C, which are mainly due to recombination of electrons and Tb3+ related hole traps centres yielding its characteristic luminescence. The experimental results indicate that optically stimulated luminescence (OSL) is due to luminescent emission of Tb3+ ions and [SiO4]4â groups. The deep traps related to the 420 °C TL peak contribute to the Tb3+ related OSL. The deep traps related to the 300-320 °C TL peak contribute to OSL associated with the luminescent emission of [SiO4]4â groups.
Combining kernel estimators in the uniform deconvolution problem
We construct a density estimator and an estimator of the distribution
function in the uniform deconvolution model. The estimators are based on
inversion formulas and kernel estimators of the density of the observations and
its derivative. Asymptotic normality and the asymptotic biases are derived
Asymptotic normality of the deconvolution kernel density estimator under the vanishing error variance
Let be i.i.d. observations, where and
the 's and 's are independent. Assume that the 's are unobservable and
that they have the density and also that the 's have a known density
Furthermore, let depend on and let as
We consider the deconvolution problem, i.e. the problem of
estimation of the density based on the sample A popular
estimator of in this setting is the deconvolution kernel density estimator.
We derive its asymptotic normality under two different assumptions on the
relation between the sequence and the sequence of bandwidths
We also consider several simulation examples which illustrate different types
of asymptotics corresponding to the derived theoretical results and which show
that there exist situations where models with have to be
preferred to the models with fixed Comment: 22 pages, 8 figure
Nonparametric volatility density estimation for discrete time models
We consider discrete time models for asset prices with a stationary
volatility process. We aim at estimating the multivariate density of this
process at a set of consecutive time instants. A Fourier type deconvolution
kernel density estimator based on the logarithm of the squared process is
proposed to estimate the volatility density. Expansions of the bias and bounds
on the variance are derived
Nonparametric methods for volatility density estimation
Stochastic volatility modelling of financial processes has become
increasingly popular. The proposed models usually contain a stationary
volatility process. We will motivate and review several nonparametric methods
for estimation of the density of the volatility process. Both models based on
discretely sampled continuous time processes and discrete time models will be
discussed.
The key insight for the analysis is a transformation of the volatility
density estimation problem to a deconvolution model for which standard methods
exist. Three type of nonparametric density estimators are reviewed: the
Fourier-type deconvolution kernel density estimator, a wavelet deconvolution
density estimator and a penalized projection estimator. The performance of
these estimators will be compared. Key words: stochastic volatility models,
deconvolution, density estimation, kernel estimator, wavelets, minimum contrast
estimation, mixin
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Practical tips for teaching mindfulness to children and adolescents in school-based settings
Mindfulness derives from Buddhist practice and is described as âthe process of engaging a full, direct, and active awareness of experienced phenomena that is spiritual in aspect and that is maintained from one moment to the nextâ (Van Gordon, Shonin, Zangeneh, & Griffiths, 2014). In a previous issue of Education and Health, we briefly reviewed research findings and discussed the growing interest amongst educational stakeholders into the applications of mindfulness for improving both the health and learning environment of school-aged children (Shonin, Van Gordon, & Griffiths, 2012). For example, mindfulness has been shown to improve levels of anxiety, depression, somatic distress, self-esteem, and sleep quality in schoolchildren with and without a psychiatric history (Biegel, Brown, Shapiro, & Schubert, 2009; Burke, 2010). Mindfulness has also been shown to improve childrensâ problematic responses to social stress (e.g., thought rumination, intrusive thoughts, emotional arousal, etc.) (Mendelson et al., 2010) as well as teacher-rated classroom social competant behaviours (Schonert-Reichl & Lawlor, 2010). Additionally, there is preliminary evidence to suggest that mindfulness can enhance metacognition and executive functioning in schoolchildren (Flook et al., 2010)
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