On second-order improved estimation of a gamma scale parameter

This paper concludes our comprehensive study on point estimation of model parameters of a gamma distribution from a second-order decision theoretic point of view. It should be noted that efficient estimation of gamma model parameters for samples ‘not large’ is a challenging task since the exact sampling distributions of the maximum likelihood estimators and its variants are not known. Estimation of a gamma scale parameter has received less attention from the earlier researchers compared to shape parameter estimation. What we have observed here is that improved estimation of the shape parameter does not necessarily lead to improved scale estimation if a natural moment condition (which is also the maximum likelihood restriction) is satisfied. Therefore, this work deals with the gamma scale parameter estimation as a separate new problem, not as a by-product of the shape parameter estimation, and studies several estimators in terms of second-order risk.     

Minimax property of stein's estimator

Stein's estimator and some other estimators of the mean of a K-variate normal distribution are known to dominate the maximum likelihood estimator under quadratic loss for K > 3, and are therefore minimax. In this paper it is shown that the minimax property of Stein's rule is preserved with respect to a generalized loss function.     

A generalization of the compound rayleigh distribution: using a bayesian method on cancer survival times

In this paper the generalized compound Rayleigh model, exhibiting flexible hazard rate, is high¬lighted. This makes it attractive for modelling survival times of patients showing characteristics of a random hazard rate. The Bayes estimators are derived for the parameters of this model and some survival time parameters from a right censored sample. This is done with respect to conjugate and discrete priors on the parameters of this model, under the squared error loss function, Varian's asymmetric linear-exponential (linex) loss function and a weighted linex loss function. The future survival time of a patient is estimated under these loss functions. A Monte Carlo simu¬lation procedure is used where closed form expressions of the estimators cannot be obtained. An example illustrates the proposed estimators for this model.     

Towards a Model of Working Groups

No abstract available for this article.     

A Different Kind of Holding Environment: A Case Study of Group Work with Pediatric Staff

ABSTRACT

This paper reports a nearly two-year intervention with staff at a major urban pediatric facility. The authors worked as trainers and consultants for two groups–the Emergency Department (ED) and Chronic Care units (CC). Following two days of training about grief and loss, highlighting skill building for work with traumatized and grieving children and parents, the consultants worked to develop teams which could provide a Winnicottian holding environment for staff. The efficacy of this staff training is analyzed and support needs of staff members are identified. Findings include varied willingness to make use of group support. Chronic care participants are more likely to express willingness to engage in on-going support for one another, while ED staff members seem to prefer structured, educational training and episodic support. The holding environment of the processing groups must be adapted to fit the needs of the medical sub-culture.     

损失厌恶、后悔厌恶与沉没成本效应的关联性分析

以往研究认为沉没成本效应的产生与损失厌恶和后悔厌恶相关,但对其相互关系的探讨较少考虑货币性沉没成本和非货币性沉没成本的不同影响.文章选取证券监管者和证券市场投资者作为被试对象,通过有情境因素的调查问卷对损失厌恶、后悔厌恶与沉没成本效应之间的关联性进行验证.实证结果发现,与证券市场投资者相比,证券监管者的损失厌恶倾向要显著更低,而二者的后悔厌恶和沉没成本效应则无显著差异;证券监管者的数据结果表明,后悔厌恶与沉没成本效应之间存在显著的相关性,而损失厌恶与沉没成本效应之间不存在显著的相关性;相比之下,证券市场投资者的数据结果则表明,损失厌恶与沉没成本之间存在显著的相关性,而后悔厌恶与沉没成本之间则不存在显著的相关性.     

Oracle Inequalities for Convex Loss Functions with Nonlinear Targets

This article considers penalized empirical loss minimization of convex loss functions with unknown target functions. Using the elastic net penalty, of which the Least Absolute Shrinkage and Selection Operator (Lasso) is a special case, we establish a finite sample oracle inequality which bounds the loss of our estimator from above with high probability. If the unknown target is linear, this inequality also provides an upper bound of the estimation error of the estimated parameter vector. Next, we use the non-asymptotic results to show that the excess loss of our estimator is asymptotically of the same order as that of the oracle. If the target is linear, we give sufficient conditions for consistency of the estimated parameter vector. We briefly discuss how a thresholded version of our estimator can be used to perform consistent variable selection. We give two examples of loss functions covered by our framework.     

Conditional VAR and Expected Shortfall: A New Functional Approach

We estimate two well-known risk measures, the value-at-risk (VAR) and the expected shortfall, conditionally to a functional variable (i.e., a random variable valued in some semi(pseudo)-metric space). We use nonparametric kernel estimation for constructing estimators of these quantities, under general dependence conditions. Theoretical properties are stated whereas practical aspects are illustrated on simulated data: nonlinear functional and GARCH(1,1) models. Some ideas on bandwidth selection using bootstrap are introduced. Finally, an empirical example is given through data of the S&P 500 time series.     

Temporal Disaggregation: Methods,Information Loss,and Diagnostics

This research provides a generalized framework to disaggregate lower-frequency time series and evaluate the disaggregation performance. The proposed framework combines two models in separate stages: a linear regression model to exploit related independent variables in the first stage and a state–space model to disaggregate the residual from the regression in the second stage. For the purpose of providing a set of practical criteria for assessing the disaggregation performance, we measure the information loss that occurs during temporal aggregation while examining what effects take place when aggregating data. To validate the proposed framework, we implement Monte Carlo simulations and provide two empirical studies. Supplementary materials for this article are available online.