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981.
Zahra Asadollahi Sohi Rassoul Noorossana Seyed Taghi Akhavan Niaki 《统计学通讯:理论与方法》2013,42(12):2242-2255
Simulation models often include a large number of input factors, many of them may be unimportant to the output; justifying the use of factor screening experiments to eliminate unimportant input factors from consideration in later stages of analysis. With a large number of factors, the challenge is designing experiments so that total number of runs and consequently the required time and cost decrease while achieving a satisfactory detection rate. This article employs frequency domain method (FDM) which is applicable in discrete-event simulation models to propose a new statistic defined as the ratio of estimated signal spectrum to maximum estimated noise spectrum. The proposed method not only has the FDM advantages compared to classic screening approaches but also helps to reduce the error of associated with distinguishing important effects from unimportant ones. Furthermore, as an alternative to the existing statistics, it is shown that not only the proposed statistic does not deteriorate the power of the screening test but in some instances it helps to improve it. 相似文献
982.
Peter Congdon 《统计学通讯:理论与方法》2013,42(11):1933-1953
Composite morbidity indices summarize geographic inequalities in disease, and are used to distribute resources. A spatial latent variable approach is developed for such an index, focusing on lung cancer in 3,141 U.S. counties. The model incorporates multiple indicators (cancer deaths and incidence), but also allows for population risk variables (area socio-economic, environmental, and smoking indicators) that affect lung cancer, and for missingness among indicators or risk variables. Selection of significant causes is illustrated, including nonadaptive and adaptive selection. To reflect geographic clustering in lung cancer, the latent morbidity index is spatially correlated, although the level of correlation is data determined. 相似文献
983.
Yu-Pin Hu 《统计学通讯:理论与方法》2013,42(8):1453-1467
The Peña–Box model is a type of dynamic factor model whose factors try to capture the time-effect movements of a multiple time series. The Peña–Box model can be expressed as a vector autoregressive (VAR) model with constraints. This article derives the maximum likelihood estimates and the likelihood ratio test of the VAR model for Gaussian processes. Then a test statistic constructed by canonical correlation coefficients is presented and adjusted for conditional heteroscedasticity. Simulations confirm the validity of adjustments for conditional heteroscedasticity, and show that the proposed statistics perform better than the statistics used in the existing literature. 相似文献
984.
This article presents a generalization of the imperfect sequential preventive maintenance (PM) policy with minimal repair. As failures occur, the system experiences one of two types of failures: a Type-I failure (minor), rectified by a minimal repair; or a Type-II failure (catastrophic) that calls for an unplanned maintenance. In each maintenance period, the system is maintained following the occurrence of a Type-II failure or at age, whichever takes place first. At the Nth maintenance, the system is replaced rather than maintained. The imperfect PM model adopted in this study incorporates with improvement factors in the hazard-rate function. Taking age-dependent minimal repair costs into consideration, the objective consists of finding the optimal PM and replacement schedule that minimize the expected cost per unit time over an infinite time-horizon. 相似文献
985.
We discuss the development of dynamic factor models for multivariate financial time series, and the incorporation of stochastic volatility components for latent factor processes. Bayesian inference and computation is developed and explored in a study of the dynamic factor structure of daily spot exchange rates for a selection of international currencies. The models are direct generalizations of univariate stochastic volatility models and represent specific varieties of models recently discussed in the growing multivariate stochastic volatility literature. We discuss model fitting based on retrospective data and sequential analysis for forward filtering and short-term forecasting. Analyses are compared with results from the much simpler method of dynamic variance-matrix discounting that, for over a decade, has been a standard approach in applied financial econometrics. We study these models in analysis, forecasting, and sequential portfolio allocation for a selected set of international exchange-rate-return time series. Our goals are to understand a range of modeling questions arising in using these factor models and to explore empirical performance in portfolio construction relative to discount approaches. We report on our experiences and conclude with comments about the practical utility of structured factor models and on future potential model extensions. 相似文献
986.
Miao-Yu Tsai 《统计学通讯:理论与方法》2013,42(16):2849-2864
The test of variance components of possibly correlated random effects in generalized linear mixed models (GLMMs) can be used to examine if there exists heterogeneous effects. The Bayesian test with Bayes factors offers a flexible method. In this article, we focus on the performance of Bayesian tests under three reference priors and a conjugate prior: an approximate uniform shrinkage prior, modified approximate Jeffreys' prior, half-normal unit information prior and Wishart prior. To compute Bayes factors, we propose a hybrid approximation approach combining a simulated version of Laplace's method and importance sampling techniques to test the variance components in GLMMs. 相似文献
987.
Herv Abdi Lynne J. Williams Domininique Valentin 《Wiley Interdisciplinary Reviews: Computational Statistics》2013,5(2):149-179
Multiple factor analysis (MFA, also called multiple factorial analysis) is an extension of principal component analysis (PCA) tailored to handle multiple data tables that measure sets of variables collected on the same observations, or, alternatively, (in dual‐MFA) multiple data tables where the same variables are measured on different sets of observations. MFA proceeds in two steps: First it computes a PCA of each data table and ‘normalizes’ each data table by dividing all its elements by the first singular value obtained from its PCA. Second, all the normalized data tables are aggregated into a grand data table that is analyzed via a (non‐normalized) PCA that gives a set of factor scores for the observations and loadings for the variables. In addition, MFA provides for each data table a set of partial factor scores for the observations that reflects the specific ‘view‐point’ of this data table. Interestingly, the common factor scores could be obtained by replacing the original normalized data tables by the normalized factor scores obtained from the PCA of each of these tables. In this article, we present MFA, review recent extensions, and illustrate it with a detailed example. WIREs Comput Stat 2013, 5:149–179. doi: 10.1002/wics.1246 This article is categorized under:
- Data: Types and Structure > Categorical Data
- Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis
- Statistical and Graphical Methods of Data Analysis > Multivariate Analysis
988.
Mauro Costantini 《Journal of applied statistics》2013,40(10):2275-2289
This paper compares the forecasting performance of three alternative factor models based on business survey data for the industrial production in Italy. The first model uses static principal component analysis, while the other two apply dynamic principal component analysis in frequency domain and subspace algorithms for state-space representation, respectively. Once the factors are extracted from the business survey data, then they are included into a single equation to predict the industrial production index. The forecast results show that the three factor models have a better performance than that of a simple autoregressive benchmark model regardless of the specification and estimation methods. Furthermore, the state-space model yields superior forecasts amongst the factor models. 相似文献
989.
基于高技术产业2000-2008年的面板数据,采用非参数的Malmquist指数分析方法,对5大行业的全要素生产率、技术效率等的变化趋势进行研究,研究结果表明全要素生产率的提高主要是由于技术进步的贡献,行业间效率变化水平差异性较为明显,自主创新能力较弱。提出优化资源配置、完善自主创新体系等政策建议,以期提升我国高技术产业效率,落实国家自主创新战略。 相似文献
990.
农户宅基地使用权流转意愿及影响因素研究——基于武汉市江夏区210户调查问卷分析 总被引：1，自引：0，他引：1
以武汉市江夏区为研究区域,通过对江夏区210个农户的实地问卷调查,首先分析了江夏区宅基地使用现状,宅基地使用权流转呈现的特点,进一步应用Logistic回归模型定量的分析农户宅基地使用权流转的影响因素。研究结果表明:户主文化程度、供养系数、非农就业率、距离城镇的距离、家庭宅基地和房屋的价值,这些因素对于农户宅基地使用权流转具有显著性的影响。 相似文献