A novel bagging approach for variable ranking and selection via a mixed importance measure

At present, ensemble learning has exhibited its great power in stabilizing and enhancing the performance of some traditional variable selection methods such as lasso and genetic algorithm. In this paper, a novel bagging ensemble method called BSSW is developed to implement variable ranking and selection in linear regression models. Its main idea is to execute stepwise search algorithm on multiple bootstrap samples. In each trial, a mixed importance measure is assigned to each variable according to the order that it is selected into final model as well as the improvement of model fitting resulted from its inclusion. Based on the importance measure averaged across some bootstrapping trials, all candidate variables are ranked and then decided to be important or not. To extend the scope of application, BSSW is extended to the situation of generalized linear models. Experiments carried out with some simulated and real data indicate that BSSW achieves better performance in most studied cases when compared with several other existing methods.     

Expected loss functions as additional measures to assess performance of multiple testing procedures for combination drug dose finding

There are several measures that are commonly used to assess performance of a multiple testing procedure (MTP). These measures include power, overall error rate (family‐wise error rate), and lack of power. In settings where the MTP is used to estimate a parameter, for example, the minimum effective dose, bias is of interest. In some studies, the parameter has a set‐like structure, and thus, bias is not well defined. Nevertheless, the accuracy of estimation is one of the essential features of an MTP in such a context. In this paper, we propose several measures based on the expected values of loss functions that resemble bias. These measures are constructed to be useful in combination drug dose response studies when the target is to identify all minimum efficacious drug combinations. One of the proposed measures allows for assigning different penalties for incorrectly overestimating and underestimating a true minimum efficacious combination. Several simple examples are considered to illustrate the proposed loss functions. Then, the expected values of these loss functions are used in a simulation study to identify the best procedure among several methods used to select the minimum efficacious combinations, where the measures take into account the investigator's preferences about possibly overestimating and/or underestimating a true minimum efficacious combination. The ideas presented in this paper can be generalized to construct measures that resemble bias in other settings. These measures can serve as an essential tool to assess performance of several methods for identifying set‐like parameters in terms of accuracy of estimation. Copyright © 2012 John Wiley & Sons, Ltd.     

Conditional logspline density estimation

In conditional logspline modelling, the logarithm of the conditional density function, log f(y|x), is modelled by using polynomial splines and their tensor products. The parameters of the model (coefficients of the spline functions) are estimated by maximizing the conditional log-likelihood function. The resulting estimate is a density function (positive and integrating to one) and is twice continuously differentiable. The estimate is used further to obtain estimates of regression and quantile functions in a natural way. An automatic procedure for selecting the number of knots and knot locations based on minimizing a variant of the AIC is developed. An example with real data is given. Finally, extensions and further applications of conditional logspline models are discussed.     

Basic concepts of multiple tests — A survey

It is the purpose of this paper to review the main aspects related to multiple test problems. This concerns among others the particularities of multiple tests as for instance the formulation of restrictions to avoid inconsistent decisions and of criteria to control for a multiple type I error rate. In addition, the basic principles for constructing multiple tests are introduced and their properties are summarized. The paper closes with giving a rough idea of further special multiple test problems and their corresponding test procedures.     

河南省人口出生率影响因素的实证分析

改革开放以来,中国经济的飞速发展在很大程度上得益于人口政策所带来的人口红利。然而随着时代的进步,中国的人口结构呈现出新的状况,即低出生率、低死亡率和低增长率。为预测我国在实施“全面二孩”政策后未来可能出现的人口结构变动趋势,以人口大省河南省作为案例研究人口出生率的影响因素。研究利用了河南省18个省辖市的数据,应用的研究方法是逐步回归方法。分析结果表明：河南省的人口出生率受到负担少年系数和政府财政在医疗、教育、社会保障与就业四个方面支出的影响,其中负担少年系数的影响最为显著。     

The Use of Resampling Methods to Simplify Regression Models in Medical Statistics

The number of variables in a regression model is often too large and a more parsimonious model may be preferred. Selection strategies (e.g. all-subset selection with various penalties for model complexity, or stepwise procedures) are widely used, but there are few analytical results about their properties. The problems of replication stability, model complexity, selection bias and an over-optimistic estimate of the predictive value of a model are discussed together with several proposals based on resampling methods. The methods are applied to data from a case–control study on atopic dermatitis and a clinical trial to compare two chemotherapy regimes by using a logistic regression and a Cox model. A recent proposal to use shrinkage factors to reduce the bias of parameter estimates caused by model building is extended to parameterwise shrinkage factors and is discussed as a further possibility to illustrate problems of models which are too complex. The results from the resampling approaches favour greater simplicity of the final regression model.     

Nonparametric Estimation for FBSDEs Models with Applications in Finance

As a continuous-time model, forward-backward stochastic differential equations (in short FBSDEs) have been successfully applied in mathematical finance, e.g., European option pricing for either a small or a large investor in a Markovian market. However, the correct FBSDEs model for a specific topic can neither be provided automatically by financial market nor derived from theory of mathematical finance. In this article, a nonparametric FBSDEs model is adopted for its flexibility and robustness, and the estimators of the functional coefficients of the FBSDEs model are obtained. The asymptotic properties of the estimators are also discussed. A simulation is performed to test the feasibility of our method.     

Models and Proposals for Malaria: A Review

Mathematical models to study the dynamics of malaria continue to be developed and upgraded on the parasite component, which is the causative agent for malaria; on the human component that serves as a reservoir of infection for the blood feeding female mosquitoes; on the disease transmitting vector, the component mostly responsible for the movement of the parasite agent from one human to another; or on the life cycle of the malaria parasite as a pathogen both within the human and vector populations. The consideration of so-far neglected features can be beneficial for the control of malaria.     

Statistical inference for the effect of magnetic brain stimulation on a motoneurone

A stochastic model is developed for the possible excitatory and inhibitory effects of a stimulus to the brain on the activity of single human motoneurones. The model consists of a Wiener process for the build-up of underlying potential, a deterministic effect due to the stimulus and a random lag from brain to muscle. Direct likelihood inference for its parameters seems impossible, and we study the use of simulation to estimate the log-likelihood for the parameters of substantive interest. Monte Carlo methods yield point and confidence interval estimates of the membrane excitability underlying excitatory and inhibitory effects. The main qualitative conclusion is that both excitatory and inhibitory effects are unambiguously present. The contribution of statistical analysis to this problem is to provide accurate and apparently reliable inference for the quantities of neurophysiological interest. More generally, our methodology has the potential to make accurate likelihood-based inferences in challenging problems, but the computational burden can be large, particularly if the model is not fully adequate for the data, as in our application.     

我国商业银行金融生态:基于市场结构与绩效的实证研究

金融生态中的生态主体构成特征表现为市场结构特征,市场结构与行业绩效的关系是产业组织理论关注的经典话题。银行的效率除了受到金融生态环境影响外,主体的生态构成特征更为重要。市场结构的变化是否必然带来赢利能力的改变,有着来自不同产业和时间序列的不尽一致的经验证据。我国商业银行业的发展处于改革的震荡期,不同的实证研究方法对其结构绩效关系给出了不同的答案。逐步回归的实证分析过程表明,我国商业银行的总资产市场集中度变化与行业绩效之间呈现出密切关系。我国商业银行改革应该注重民营银行对市场结构的调整作用。