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排序方式: 共有368条查询结果,搜索用时 15 毫秒
1.
Financial stress index (FSI) is considered to be an important risk management tool to quantify financial vulnerabilities. This paper proposes a new framework based on a hybrid classifier model that integrates rough set theory (RST), FSI, support vector regression (SVR) and a control chart to identify stressed periods. First, the RST method is applied to select variables. The outputs are used as input data for FSI–SVR computation. Empirical analysis is conducted based on monthly FSI of the Federal Reserve Bank of Saint Louis from January 1992 to June 2011. A comparison study is performed between FSI based on the principal component analysis and FSI–SVR. A control chart based on FSI–SVR and extreme value theory is proposed to identify the extremely stressed periods. Our approach identified different stressed periods including internet bubble, subprime crisis and actual financial stress episodes, along with the calmest periods, agreeing with those given by Federal Reserve System reports. 相似文献
2.
Amy H. Herring Joseph G. Ibrahim Stuart R. Lipsitz 《Journal of the Royal Statistical Society. Series C, Applied statistics》2004,53(2):293-310
Summary. Non-ignorable missing data, a serious problem in both clinical trials and observational studies, can lead to biased inferences. Quality-of-life measures have become increasingly popular in clinical trials. However, these measures are often incompletely observed, and investigators may suspect that missing quality-of-life data are likely to be non-ignorable. Although several recent references have addressed missing covariates in survival analysis, they all required the assumption that missingness is at random or that all covariates are discrete. We present a method for estimating the parameters in the Cox proportional hazards model when missing covariates may be non-ignorable and continuous or discrete. Our method is useful in reducing the bias and improving efficiency in the presence of missing data. The methodology clearly specifies assumptions about the missing data mechanism and, through sensitivity analysis, helps investigators to understand the potential effect of missing data on study results. 相似文献
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Transductive methods are useful in prediction problems when the training dataset is composed of a large number of unlabeled observations and a smaller number of labeled observations. In this paper, we propose an approach for developing transductive prediction procedures that are able to take advantage of the sparsity in the high dimensional linear regression. More precisely, we define transductive versions of the LASSO (Tibshirani, 1996) and the Dantzig Selector (Candès and Tao, 2007). These procedures combine labeled and unlabeled observations of the training dataset to produce a prediction for the unlabeled observations. We propose an experimental study of the transductive estimators that shows that they improve the LASSO and Dantzig Selector in many situations, and particularly in high dimensional problems when the predictors are correlated. We then provide non-asymptotic theoretical guarantees for these estimation methods. Interestingly, our theoretical results show that the Transductive LASSO and Dantzig Selector satisfy sparsity inequalities under weaker assumptions than those required for the “original” LASSO. 相似文献
6.
Ibrahim Mike Okumu Edward Bbaale 《Development policy review : the journal of the Overseas Development Institute》2019,37(6):735-749
This article undertakes a diagnostic study of the Technical and Vocational Education and Training (TVET) sub‐sector in Uganda, with a view to characterizing the sub‐sector and identifying its potential strengths and weakness. We undertook a cross‐sectional pre‐survey of purposively selected key stakeholders in the TVET sub‐sector. We selected performance indicators following their importance in influencing the TVET reform process. Both qualitative and quantitative data was solicited from the stakeholders. Quantitative data was collected through stakeholder‐specific structured questionnaires, whilst qualitative data was collected through desk review and field visits, individual focused interviews and focus group discussions. Our findings indicate that financing and planning constraints have resulted in poor quality equipment, under‐ and ill‐trained staff, limited adoption of a competence‐based education and training (CBET) curriculum, not to mention the supervision inadequacies of TVET institutions. Besides, the limited TVET sub‐sector interaction with the private sector has incapacitated TVET curriculum development to nurture skills demanded by the private sector. Furthermore, backward technology use in the private sector has equally inhibited the success of student attachment programmes. Finally, legal ambiguities have perpetuated a qualification jungle and overlapping curricula. 相似文献
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Mohamed Tahir 《统计学通讯:理论与方法》2013,42(12):4501-4509
Non-linear renewal theory is used to derive second order asymptotic expansions for the coverage probability of a fixed-width sequential confidence interval for an unknown parameter xin the inverse linear regression model. These expansions are obtained for a two-stage sequential procedure, proposed by Perng and Tong (1974) for the construction of a confidence interval for x. 相似文献
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The purpose of the article is, in case of one sample, to obtain tests concerning the parameter in the power series distribution in one parameter using Ku11back-Leibier information measure. The class of power series distibutions contains a host of discrete distributions. Ve illustrate the general results obtained in case of the geometric distibution. 相似文献
10.
Mohamed Reda El Amri Cline Helbert Olivier Lepreux Miguel Munoz Zuniga Clmentine Prieur Delphine Sinoquet 《Statistics and Computing》2020,30(3):525-541
In this paper, we propose a new methodology for solving stochastic inversion problems through computer experiments, the stochasticity being driven by a functional random variables. This study is motivated by an automotive application. In this context, the simulator code takes a double set of simulation inputs: deterministic control variables and functional uncertain variables. This framework is characterized by two features. The first one is the high computational cost of simulations. The second is that the probability distribution of the functional input is only known through a finite set of realizations. In our context, the inversion problem is formulated by considering the expectation over the functional random variable. We aim at solving this problem by evaluating the model on a design, whose adaptive construction combines the so-called stepwise uncertainty reduction methodology with a strategy for an efficient expectation estimation. Two greedy strategies are introduced to sequentially estimate the expectation over the functional uncertain variable by adaptively selecting curves from the initial set of realizations. Both of these strategies consider functional principal component analysis as a dimensionality reduction technique assuming that the realizations of the functional input are independent realizations of the same continuous stochastic process. The first strategy is based on a greedy approach for functional data-driven quantization, while the second one is linked to the notion of space-filling design. Functional PCA is used as an intermediate step. For each point of the design built in the reduced space, we select the corresponding curve from the sample of available curves, thus guaranteeing the robustness of the procedure to dimension reduction. The whole methodology is illustrated and calibrated on an analytical example. It is then applied on the automotive industrial test case where we aim at identifying the set of control parameters leading to meet the pollutant emission standards of a vehicle. 相似文献