Horwitz's rule, transforming both sides and the design of experiments for mechanistic models

Summary. The paper develops methods for the design of experiments for mechanistic models when the response must be transformed to achieve symmetry and constant variance. The power transformation that is used is partially justified by a rule in analytical chemistry. Because of the nature of the relationship between the response and the mechanistic model, it is necessary to transform both sides of the model. Expressions are given for the parameter sensitivities in the transformed model and examples are given of optimum designs, not only for single-response models, but also for experiments in which multivariate responses are measured and for experiments in which the model is defined by a set of differential equations which cannot be solved analytically. The extension to designs for checking models is discussed.     

WEIGHTED SUMS OF NEGATIVELY ASSOCIATED RANDOM VARIABLES

In this paper, we establish strong laws for weighted sums of negatively associated (NA) random variables which have a higher‐order moment condition. Some results of Bai Z.D. & Cheng P.E. (2000) [Marcinkiewicz strong laws for linear statistics. Statist. and Probab. Lett. 43, 105–112,] and Sung S.K. (2001) [Strong laws for weighted sums of i.i.d. random variables, Statist. and Probab. Lett. 52, 413–419] are sharpened and extended from the independent identically distributed case to the NA setting. Also, one of the results of Li D.L. et al. (1995) [Complete convergence and almost sure convergence of weighted sums of random variables. J. Theoret. Probab. 8, 49–76,] is complemented and extended.     

中国FDI进入模式的影响因素

本文综合交易费用理论和谈判力量理论分析框架,探讨了FDI进入中国市场模式(合资／独资)的影响因素。应用两分Logit模型进行实证分析,结果显示区位因素、来源地因素、投资项目的经营期限以及投资规模都对FDI进入中国市场模式产生了重要的影响,而产业因素的影响则统计不显著。     

Sum,product and ratio of Pareto and gamma variables

The exact distributions of X+Y, X Y and X/(X+Y) are studied when X and Y are independent Pareto and gamma random variables. Applications are discussed, to real problems in clinical trials, computer networks and economics.     

Simulating random variables using moment-generating functions and the saddlepoint approximation

When we are given only a transform such as the moment-generating function of a distribution, it is rare that we can efficiently simulate random variables. Possible approaches such as the inverse transform using numerical inversion of the transform are computationally very expensive. However, the saddlepoint approximation is known to be exact for the Normal, Gamma, and inverse Gaussian distribution and remarkably accurate for a large number of others. We explore the efficient use of the saddlepoint approximation for simulating distributions and provide three examples of the accuracy of these simulations.     

Small area estimation strategies for large population surveys: a comparison of design and model-based methods

Small area estimation (SAE) concerns with how to reliably estimate population quantities of interest when some areas or domains have very limited samples. This is an important issue in large population surveys, because the geographical areas or groups with only small samples or even no samples are often of interest to researchers and policy-makers. For example, large population health surveys, such as Behavioural Risk Factor Surveillance System and Ohio Mecaid Assessment Survey (OMAS), are regularly conducted for monitoring insurance coverage and healthcare utilization. Classic approaches usually provide accurate estimators at the state level or large geographical region level, but they fail to provide reliable estimators for many rural counties where the samples are sparse. Moreover, a systematic evaluation of the performances of the SAE methods in real-world setting is lacking in the literature. In this paper, we propose a Bayesian hierarchical model with constraints on the parameter space and show that it provides superior estimators for county-level adult uninsured rates in Ohio based on the 2012 OMAS data. Furthermore, we perform extensive simulation studies to compare our methods with a collection of common SAE strategies, including direct estimators, synthetic estimators, composite estimators, and Datta GS, Ghosh M, Steorts R, Maples J.'s [Bayesian benchmarking with applications to small area estimation. Test 2011;20(3):574–588] Bayesian hierarchical model-based estimators. To set a fair basis for comparison, we generate our simulation data with characteristics mimicking the real OMAS data, so that neither model-based nor design-based strategies use the true model specification. The estimators based on our proposed model are shown to outperform other estimators for small areas in both simulation study and real data analysis.     

Optimal designs for additive mixture model with heteroscedastic errors

This paper presents a study of D- and A-optimality of direct sum designs for additive mixture models when the errors are heteroscedastic. Sufficient conditions are given so that D- and A-optimal designs for additive mixture models can be constructed from the D- and A-optimal designs for homogeneous models in sub-mixture systems.     

The k-factor GARMA Process with Infinite Variance Innovations

In this article, we develop the theory of k-factor Gegenbauer Autoregressive Moving Average (GARMA) process with infinite variance innovations which is a generalization of the stable seasonal fractional Autoregressive Integrated Moving Average (ARIMA) model introduced by Diongue et al. (2008 Diongue, A.K., Guégan, D. (2008). Estimation of k-Factor GIGARCH Process: A Monte Carlo Study. Communications in Statistics-Simulation and Computation 37:2037–2049.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). Stationarity and invertibility conditions of this new model are derived. Conditional Sum of Squares (CSS) and Markov Chains Monte Carlo (MCMC) Whittle methods are investigated for parameter estimation. Monte Carlo simulations are also used to evaluate the finite sample performance of these estimation techniques. Finally, the usefulness of the model is corroborated with the application to streamflow data for Senegal River at Bakel.     

户籍类型与城乡居民医疗服务利用 ——基于Andersen模型的经验分析

本文利用中国社会科学院经济研究所2018年城乡居民入户调查数据,从户籍改革角度分析户口类型改变对城乡居民医疗服务利用的影响。研究发现:与户口类型未发生改变的城乡居民相比,户口类型发生过改变(农转非)的城乡居民门急诊服务利用的可能性更高,住院医疗服务支出水平更低。从户籍改革与参保类型交互效应看,相对于户口类型未发生过改变的居民医保参保人群,户口类型发生改变(农转非)会显著提高居民医保参保人群门急诊服务利用,同时降低其住院服务利用。进一步采用PSM方法进行稳健性检验,户籍改革对提高城乡居民门急诊服务利用,降低住院医疗服务利用的结论依然成立。此外,本文还发现,直接结算提高了城乡居民门急诊和住院的就诊概率;健康自评状况越差,门急诊和住院医疗服务利用可能性越高,门急诊医疗支出、住院医疗支出和跨省异地就医支出也越高。本文认为,相对于推行基层分级诊疗制度,以户籍改革推动农村居民自由流入医疗资源更丰富的城镇地区,改革基层卫生院制度,以及改变基层医疗服务提供激励机制,可能更有助于改善农村居民健康状况。     

Closure property of consistently varying random variables based on precise large deviation principles

Let X = {X₁, X₂, …} be a sequence of independent but not necessarily identically distributed random variables, and let η be a counting random variable independent of X. Consider randomly stopped sum S_η = ∑^η_{k = 1}X_k and random maximum S_(η) ? max?{S₀, …, S_η}. Assuming that each X_k belongs to the class of consistently varying distributions, on the basis of the well-known precise large deviation principles, we prove that the distributions of S_η and S_(η) belong to the same class under some mild conditions. Our approach is new and the obtained results are further studies of Kizinevi?, Sprindys, and ?iaulys (2016) and Andrulyt?, Manstavi?ius, and ?iaulys (2017).