Model Selection for Complex Multilevel Latent Class Model

Multilevel latent class analysis is conducive to providing more effective information on both individual and group typologies. However, model selection issues still need further investigation. Current study probed into issue of high-level class numeration for a more complex model using AIC, AIC3, BIC, and BIC*. Data simulation was conducted and its result was verified by empirical data. The result demonstrated that these criteria have a certain inclination relative to sample sizes. Sample size per group plays an evident role in improving accuracy of AIC3 and BIC. The complex model requires more sample size per group to ensure accurate class numeration.     

Choosing Between the Sample-Selection Model and the Multi-Part Model

Hay and Olsen (1984) incorrectly argue that a multi-part model, the two-part model used in Duan et al. (1982,1983), is nested within the sample-selection model. Their proof relies on an unmentioned restrictive assumption that cannot be satisfied. We provide a counterexample to show that the propensity to use medical care and the level of expense can be positively associated in the two-part model, contrary to their assertion. The conditional specification in the multi-part model is preferable to the unconditional specification in the selection model for modeling actual (v. potential) outcomes. The selection model also has poor statistical and numerical properties and relies on untestable assumptions. Empirically the multi-part estimators perform as well as or better than the sample selection estimator for the data set analyzed in Duan et al. (1982, 1983).     

多水平模型及静态面板数据模型的比较研究

对两水平模型与静态面板数据模型进行对比分析：多水平模型主要用于分析具有层次结构的统计数据,面板数据模型是针对面板数据而提出的一种应用广泛的计量经济模型。面板数据可以看成是具有截面水平与时间水平的两层数据,两水平模型也能对面板数据进行分析,在一定条件下具有一定的相似性。因此,提出多水平的静态面板数据模型,为分析具有多个层次结构的面板数据提供分析工具。     

The Cgmanova Model

The Completely General MANOVA (CGMANOVA) model may be used to analyze many complex designs including the GMANOVA, EGMANOVA, MSUR models, the multivariate seemingly unrelated growth curve model, and numerous other designs that do not have closed form solutions using the likelihood ratio method. In this paper we review the theory of the CGMANOVA model and compare 11near model likelihood test results with the Wald statistic.     

The Self-Weighting Model

In this brief article, we present the Self-Weighting Model (SWM), a new weighting model for statistical analysis. SWM allows within/between-set comparisons, producing estimates with a discriminatory power not found through current weighting strategies. The model is applicable to a wide range of statistical problems for which conditional weighted means are required.     

Focused Information Criteria,Model Selection,and Model Averaging in a Tobit Model With a Nonzero Threshold

Claeskens and Hjort (2003 Claeskens, G. and Hjort, N. L. 2003. “The Focused Information Criterion”. Journal of the American Statistical Association, 98: 900–945. (with discussion)[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) have developed a focused information criterion (FIC) for model selection that selects different models based on different focused functions with those functions tailored to the parameters singled out for interest. Hjort and Claeskens (2003 Hjort, N. L. and Claeskens, G. 2003. “Frequentist Model Average Estimators”. Journal of the American Statistical Association, 98: 879–899. (with discussion)[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) also have presented model averaging as an alternative to model selection, and suggested a local misspecification framework for studying the limiting distributions and asymptotic risk properties of post-model selection and model average estimators in parametric models. Despite the burgeoning literature on Tobit models, little work has been done on model selection explicitly in the Tobit context. In this article we propose FICs for variable selection allowing for such measures as mean absolute deviation, mean squared error, and expected expected linear exponential errors in a type I Tobit model with an unknown threshold. We also develop a model average Tobit estimator using values of a smoothed version of the FIC as weights. We study the finite-sample performance of model selection and model average estimators resulting from various FICs via a Monte Carlo experiment, and demonstrate the possibility of using a model screening procedure before combining the models. Finally, we present an example from a well-known study on married women's working hours to illustrate the estimation methods discussed. This article has supplementary material online.     

关于投入产出扩展模型的探讨

在国民经济综合平衡分析中,投入产出分析方法愈来愈被广泛运用。目前运用较多的并且比较成熟的是投入产出静态模型和动态模型,然而,要完整地反映国民经济综合平衡的状况,只停留在静态模型和动态模型上显然是不够的,应当再加扩展,即建立投入产出扩展模型(以下简称扩展模型)。     

A Non-stationary Cox Model

The purpose of this paper is to consider the problem of statistical inference about a hazard rate function that is specified as the product of a parametric regression part and a non-parametric baseline hazard. Unlike Cox's proportional hazard model, the baseline hazard not only depends on the duration variable, but also on the starting date of the phenomenon of interest. We propose a new estimator of the regression parameter which allows for non-stationarity in the hazard rate. We show that it is asymptotically normal at root- n and that its asymptotic variance attains the information bound for estimation of the regression coefficient. We also consider an estimator of the integrated baseline hazard, and determine its asymptotic properties. The finite sample performance of our estimators are studied.     

The Alpha-power Tobit Model

The main object of this article is to propose an extension of the tobit model for which the error distribution follows the power-normal distribution (Gupta and Gupta, 2008 Gupta , D. , Gupta , R. C. ( 2008 ). Analyzing skewed data by power normal model . Test 17 : 197 – 210 .[Crossref], [Web of Science ®] , [Google Scholar]). Inference is dealt with by using the likelihood approach. Simulation studies and application to a real data set are used to demonstrate the usefulness of the extension.     

Asymptotically Uniform Tests After Consistent Model Selection in the Linear Regression Model

Abstract

This article specializes the critical value (CV) methods that are based upon (refinements of) Bonferroni bounds, introduced by McCloskey to a problem of inference after consistent model selection in a general linear regression model. The post-selection problem is formulated to mimic common empirical practice and is applicable to both cross-sectional and time series contexts. We provide algorithms for constructing the CVs in this setting and establish uniform asymptotic size results for the resulting tests. The practical implementation of the CVs is illustrated in an empirical application to the effect of classroom size on test scores.     

The Dynamic Model for Cancer Relapse Based on Two-Stage Model of Carcinogenesis

In this article, the time from the start of chemotherapy randomization until cancer relapse is of primary interest. Here, cancer relapse refers to the appearance of the first observable malignant clone after therapy. A dynamic model for cancer relapse after chemotherapy is developed. The model differs from the traditional cure rate models in that it takes into consideration the growth kinetics of malignant tumors using a two-stage carcinogenesis model. The survival and hazard functions for cancer relapse time are derived, and a simulation study is performed to validate the underlying model.     

Model robust confidence intervals

Confidence intervals are constructed for real-valued parameter estimation in a general regression model with normal errors. When the error variance is known these intervals are optimal (in the sense of minimizing length subject to guaranteed probability of coverage) among all intervals estimates which are centered at a linear estimate of the parameter. When the error variance is unknown and the regression model is an approximately linear model (a class of models which permits bounded systematic departures from an underlying ideal model) then an independent estimate of variance is found and the intervals can then be appropriately scaled.     

Model robust extrapolation designs

We seek designs which are optimal in some sense for extrapolation when the true regression function is in a certain class of regression functions. More precisely, the class is defined to be the collection of regression functions such that its (h + 1)-th derivative is bounded. The class can be viewed as representing possible departures from an ‘ideal’ model and thus describes a model robust setting. The estimates are restricted to be linear and the designs are restricted to be with minimal number of points. The design and estimate sought is minimax for mean square error. The optimal designs for cases X = [0, ∞] and X = [-1, 1], where X is the place where observations can be taken, are discussed.     

改进的随机截尾模型

在随机截尾模型的基础上,为了保护被调查者的隐私,文章提出了一种改进的调查数量敏感性问题的随机化回答方法,并把改进模型的精度与随机截尾模型、随机截尾Warner模型进行了比较。     

Model averaging for M-estimation

M-estimation is a widely used technique for robust statistical inference. In this paper, we study model selection and model averaging for M-estimation to simultaneously improve the coverage probability of confidence intervals of the parameters of interest and reduce the impact of heavy-tailed errors or outliers in the response. Under general conditions, we develop robust versions of the focused information criterion and a frequentist model average estimator for M-estimation, and we examine their theoretical properties. In addition, we carry out extensive simulation studies as well as two real examples to assess the performance of our new procedure, and find that the proposed method produces satisfactory results.     

基于自适应组LASSO估计的高维门限因子模型一致选择

门限因子模型设定载荷具有阈值型区制转换结构,可以同时刻画高维时间序列的共变性和区制转换特征。针对高维门限因子模型,本文基于自适应组LASSO技术给出了一种一致模型选择过程。这一模型选择过程将因子个数设定、门限效应推断纳入统一的分析框架,不仅解决了模型选择的一致性问题,还同时实现了模型选择误差的统一控制,这对于高维门限因子模型而言是非常重要的。理论研究和随机模拟结论表明本文给出的一致模型选择过程具有良好的大样本性质和有限样本表现。最后,本文将门限因子模型应用于我国金融市场分析,实证结果进一步验证了本文理论的有效性。     

Lassoing the HAR Model: A Model Selection Perspective on Realized Volatility Dynamics

Realized volatility computed from high-frequency data is an important measure for many applications in finance, and its dynamics have been widely investigated. Recent notable advances that perform well include the heterogeneous autoregressive (HAR) model which can approximate long memory, is very parsimonious, is easy to estimate, and features good out-of-sample performance. We prove that the least absolute shrinkage and selection operator (Lasso) recovers the lags structure of the HAR model asymptotically if it is the true model, and we present Monte Carlo evidence in finite samples. The HAR model's lags structure is not fully in agreement with the one found using the Lasso on real data. Moreover, we provide empirical evidence that there are two clear breaks in structure for most of the assets we consider. These results bring into question the appropriateness of the HAR model for realized volatility. Finally, in an out-of-sample analysis, we show equal performance of the HAR model and the Lasso approach.     

Markowitz模型与Black—Litterman投资人情绪对资产组合的影响模型比较研究

由于马尔科维茨资产组合模型存在放大方差和对输入变量异常敏感以及无卖空约束下的无意义的权重问题,使均值方差模型很难在实际中应用。针对该模型的缺陷,利用BMA模型优化Black-Litterman模型观点加入方式,详细设定了模型观点矩阵,利用非贝叶斯法则构建了投资人对观念的置信度,明确计算了隐含均衡收益和上证指数数据,再次证实了投资人情绪对传统资产组合模型结果的影响,并从数理模型上阐释了两个模型所依据的理论基础。