注意性告知与元认知策略对培训效果的影响研究

计算机审计培训是培养高素质审计人才的有效途径。注意性告知和元认知策略是影响计算机审计培训效果的重要因素。实证研究了注意性告知与元认知策略对培训效果的影响,研究发现:注意性告知与元认知策略都会对培训效果产生正向影响;注意性告知还会对元认知策略产生正向影响。此外,注意性告知通过元认知策略部分中介作用于培训效果。     

Comment: Administrative Statistics: A BLS Perspective

A semiparametric, information-based estimator is used to estimate strategies in prices and advertising for Coca-Cola and Pepsi-Cola. Separate strategies for each firm are estimated with and without restrictions from game theory. These information/entropy estimators are consistent and efficient. These estimates are used to test theories about the strategies of firms and to see how changes in incomes or factor prices affect these strategies.     

RANDOMIZED SEARCH STRATEGIES FOR FINDING OPTIMAL OR NEAR OPTIMAL BLOCK AND ROW-COLUMN DESIGNS

We consider a family of effective and efficient strategies for generating experimental designs of several types with high efficiency. These strategies employ randomized search directions and at some stages allow the possibility of taking steps in a direction of decreasing efficiency in an effort to avoid local optima. Hence our strategies have some affinity with the simulated annealing algorithm of combinatorial optimization. The methods work well and compare favourably with other search strategies. We have implemented them for incomplete block designs, optionally resolvable, and for row-column designs.     

适应性市场假说:来自中国商品期货市场的证据

适应性市场假说认为交易策略随交易者行为适应环境变化而演化。研究10种技术交易策略在中国商品期货上的业绩表现,来检验是否符合适应性市场假说。实证结果显示简单知名的策略如MACD、RSI、BOLL、MA+MACD平均收益为负,复杂冷门的策略如MA组合、KD、SAR、ADX+KD、MA(5,20)、ATR平均收益显著为正,且交易策略绩效随价格趋势周期性波动。总之,技术交易规则超额收益在中国商品期货市场依然存在,结论和适应性市场假说一致。     

Multiple tests for the performance of different investment strategies

In the context of modern portfolio theory, we compare the out-of-sample performance of eight investment strategies which are based on statistical methods with the out-of-sample performance of a family of trivial strategies. A?wide range of approaches is considered in this work, including the traditional sample-based approach, several minimum-variance techniques, a shrinkage, and a minimax approach. In contrast to similar studies in the literature, we also consider short-selling constraints and a risk-free asset. We provide a way to extend the concept of minimum-variance strategies in the context of short-selling constraints. A main drawback of most empirical studies on that topic is the use of simple testing procedures which do not account for the effects of multiple testing. For that reason we conduct several hypothesis tests which are proposed in the multiple-testing literature. We test whether it is possible to beat a trivial strategy by at least one of the non-trivial strategies, whether the trivial strategy is better than every non-trivial strategy, and which of the non-trivial strategies is significantly outperformed by naive diversification. The empirical part of our study is conducted using US stock returns from the last four decades, obtained via the CRSP database.     

The design of a clinical trial with several patient categories

Various strategies are investigated for selecting one of two medical treatments when patients may be divided into k ≥ 2 categories on the basis of their expected differences in response to the two treatments. The strategies compared are (1) k independent decisions, (2) a single overall decision made on the basis of simple pooling, and (3) the Bayes strategy. The optimal clinical trial size is supplied for each strategy, and conditions delineated under which each of the strategies is to be preferred.     

Implementing optimal allocation for sequential continuous responses with multiple treatments

In practice, it is important to find optimal allocation strategies for continuous response with multiple treatments under some optimization criteria. In this article, we focus on exponential responses. For a multivariate test of homogeneity, we obtain the optimal allocation strategies to maximize power while (1) fixing sample size and (2) fixing expected total responses. Then the doubly adaptive biased coin design [Hu, F., Zhang, L.-X., 2004. Asymptotic properties of doubly adaptive biased coin designs for multi-treatment clinical trials. The Annals of Statistics 21, 268–301] is used to implement the optimal allocation strategies. Simulation results show that the proposed procedures have advantages over complete randomization with respect to both inferential (power) and ethical standpoints on average. It is important to note that one can usually implement optimal allocation strategies numerically for other continuous responses, though it is usually not easy to get the closed form of the optimal allocation theoretically.     

A note on infinite-armed Bernoulli bandit problems with generalized beta prior distributions

A bandit problem with infinitely many Bernoulli arms is considered. The parameters of Bernoulli arms are independent and identically distributed random variables from a generalized beta distributionG3B(a, b, λ) witha, b>0 and 0<λ<2. Under the generalized beta prior distributions, we first derive the asymptotic expected failure rates ofk-failure strategies, and then obtain a lower bound for the expected failure rate over all strategies investigated in Berry et al. (1997). The asymptotic expected failure rates for the other three strategies studied in Berry et al. (1997) are also included. Numerical estimations for a variety of generalized beta prior distributions are presented to illustrate the performances of these strategies.     

Adaptive web sampling in ecology

Adaptive sampling strategies for ecological and environmental studies are described in this paper. The motivations for adaptive sampling are discussed. Developments in this area over recent decades are reviewed. Adaptive cluster sampling and a number of its variations are described. The newer class of adaptive web sampling designs and their spatial sampling uses are discussed. Case studies in the use of adaptive sampling strategies with ecological populations are cited. The nature of optimal sampling strategies is described. Design-based and model-based approaches to inference with adaptive sampling strategies are summarized.     

On conditional sampling strategies

Estimation of the population average by means of a conditional strategy has been considered e.g. in [2–6,9] and [10]. Let us assume that the sampling design depends on a function of an auxiliary variable called an auxiliary statistic like: the sample mean or the sample variance. Under the conditional versions of these designs several estimators of the population mean are considered: the Horvitz-Thompson statistic, ratio and regression type estimators. The unbiasedness and accuracy of the strategies are considered. The empirical example of the accuracy comparisons of strategies is developed. The conditional strategies could improve the accuracy of estimation and protect it against outliers observations.     

Re-Defining the Who,When, and Where of Mentoring for Professional Statisticians

Organizations tailor their mentoring strategies to accommodate internal resources and preferences, producing different approaches in academic, government, and corporate environments. Across these settings, three common barriers impede effective mentoring of statisticians: overspecialization, time constraints, and geographic dispersion. The authors share mentoring strategies that have emerged at their organization, Mathematica Policy Research, to overcome these obstacles. Practices include creating a methodology working group to unite researchers with diverse backgrounds, integrating mentoring into existing workflows, and harnessing modern technological infrastructure to facilitate virtual mentoring. Although these strategies emerged within a specific professional context, they suggest opportunities for statisticians to expand the channels through which mentorship can occur.     

Strategies for a sequential selection of the better of two trinomial populations

A sequential procedure for a selection of the better of two trinomial populations has been proposed by ?idók (1988). The present paper shows some Monte Carlo results for 4 different strategies of sequential experimentation in this procedure, on this basis compares the strategies, and gives some practical recommendations for choosing the strategy.     

Sequential importance sampling for nonparametric Bayes models: The next generation

There are two generations of Gibbs sampling methods for semiparametric models involving the Dirichlet process. The first generation suffered from a severe drawback: the locations of the clusters, or groups of parameters, could essentially become fixed, moving only rarely. Two strategies that have been proposed to create the second generation of Gibbs samplers are integration and appending a second stage to the Gibbs sampler wherein the cluster locations are moved. We show that these same strategies are easily implemented for the sequential importance sampler, and that the first strategy dramatically improves results. As in the case of Gibbs sampling, these strategies are applicable to a much wider class of models. They are shown to provide more uniform importance sampling weights and lead to additional Rao-Blackwellization of estimators.     

Model-complete strategies for sampling from convex autocorrelated finite populations

Sampling from finite populations when there is autocorrelation between the population units, is the subject of study in this paper. The case when the autocorrelation function for the population is convex is examined. We provide, in the first instance, the best unbiased predictor of the population mean under the assumptions of the assumed model. For this predictor, the optimum class of sampling strategies under the model-complete criterion is determined. For practical applications, a subclass of the above class of strategies is considered and it is shown in Section 4 that the centrally located is the optimal one. Finally, some numerical examples and comparison study has been carried out in order to see, in practice, the merit of the optimal strategies suggested in the present paper.     

Softly shrunk and partially shrunk rank-reduced estimation of the regression coefficients

In this paper we introduce strategies how the ordinary least square estimator of the coefficient vector of the multiple regression could be shrunk. One of the strategies shrinks all components while the other shrinks only some components. We show that the proposed shrunken estimators are admissibile. We also provide theoretical results on risk comparisons.     

Conditional simulation from highly structured Gaussian systems, with application to blocking-MCMC for the Bayesian analysis of very large linear models

This paper examines strategies for simulating exactly from large Gaussian linear models conditional on some Gaussian observations. Local computation strategies based on the conditional independence structure of the model are developed in order to reduce costs associated with storage and computation. Application of these algorithms to simulation from nested hierarchical linear models is considered, and the construction of efficient MCMC schemes for Bayesian inference in high-dimensional linear models is outlined.     

An empirical study on technical analysis: GARCH (1, 1) model

One of the deficits of the common Bollinger band is that it fails to consider the fat tails/leptokurtosis often exists in financial time series. An adjusted Bollinger band generated by rolling GARCH regression method is proposed in this study. The performance of the adjusted Bollinger band strategy on EUR, GBP, JPY, and AUD vs. USD foreign exchange trading is evaluated. Results show that in general, the adjusted Bollinger band performs better than the traditional one in terms of success ratios, net successes, and profit. In addition, no matter there is transaction cost or not, only adjusted Bollinger strategies are recommended for investors. Adjusted Bollinger band strategies with MA 5 or 10 are recommended for EUR, GBP, and JPY. Adjusted Bollinger strategy with MA 20 is the recommended strategies for AUD.     

Time-Varying Smooth Transition Autoregressive Models

Nonlinear regime-switching behavior and structural change are often perceived as competing alternatives to linearity. In this article we study the so-called time-varying smooth transition autoregressive (TV-STAR) model, which can be used both for describing simultaneous nonlinearity and structural change and for distinguishing between these features. Two modeling strategies for empirical specification of TV-STAR models are developed. Monte Carlo simulations show that neither of the two strategies dominates the other. A specific-to-general-to-specific procedure is best suited for obtaining a first impression of the importance of nonlinearity and/or structural change for a particular time series. A specific-to-general procedure is most useful in careful specification of a model with nonlinear and/or time-varying properties. An empirical application to a large dataset of U.S. macroeconomic time series illustrates the relative merits of both modeling strategies.     

Principles and analysis of hematopoietic stem cell transplantation outcomes: the physician’s perspective

Hematopoietic stem cell transplantation (HSCT) is used widely for the treatment of malignant and non-malignant diseases. Advances in transplantation strategies such as modifications to existing transplant-conditioning regimens to lower morbidity and mortality, use of alternative donors and grafts, supportive care including graft-versus-host disease prophylaxis and antimicrobial therapy have led to increasing numbers of HSCT being performed. The procedure is now available to children, young and older adults with an ever increasing number of survivors from this procedure. Analysis of outcomes after HSCT giving consideration to existing strategies warrants using appropriate statistical methodology. This is critical not only in documenting the current success of HSCT but to plan future strategies to optimize accessibility to HSCT, donor and graft choices, patient selection, counsel potential HSCT candidates and long-term survivors and plan intervention trials for these persons. We describe concepts and principles of HSCT to better understand the complexities associated with outcome analysis.     

ULM,a software for conservation and evolutionary biologists

Matrix models for population dynamics have recently been studied intensively and have many applications to theoretical and applied problems (conservation, management). The computer program ULM (Unified Life Models) collects a good part of the actual knowledge on the subject. It is a powerful tool to study the life cycle of species and meta-populations. In the general framework of discrete dynamical systems and symbolic computation, simple commands and convenient graphics are provided to assist the biologist. The main features of the program are shown through detailed examples: a simple model of a starling population life cycle is first presented leading to basic concepts (growth rates, stable age distribution, sensitivities); the same model is used to study competing strategies in a varying environment (extinction probabilities, stochastic sensitivities); a meta-population model with migrations is then presented; some results on migration strategies and evolutionary stable strategies are eventually proposed.