Maximum principle for optimal control of anticipated forward–backward stochastic differential delayed systems with regime switching

This paper is concerned with a Pontryagin maximum principle for optimal control problem of stochastic system, which is described by an anticipated forward–backward stochastic differential delayed equation and modulated by a continuous‐time finite‐state Markov chain. We establish a necessary maximum principle and sufficient verification theorem for the optimal control by virtue of the duality method and convex analysis. To illustrate the theoretical results, we apply them to a recursive utility investment‐consumption problem, and the optimal consumption rate is derived explicitly. Copyright © 2015 John Wiley & Sons, Ltd.     

有效连接分析在脑功能磁共振数据中的发展和应用

大脑是一个高度复杂的神经网络系统,随着磁共振成像技术的快速发展,目前研究逐步深入到人类大脑并行神经网络和不同脑区间的信息流,以更全面深入地探索脑功能机制。有效连接分析是近年来在脑功能网络方面的一个研究热点。对人脑功能网络进行有效连接分析,可能对神经精神疾病的病理机制和脑功能异常状态下的功能逻辑有着更好的理解。本文着重对有效连接的几种经典方法(包括结构方程模型、多变量自回归模型、格兰杰因果分析和动态因果模型)的算法原理、存在的问题、算法比较、最新发展及在大脑功能磁共振成像数据中的应用做简要介绍。     

婴幼儿潮气呼吸肺功能指标的预计公式模型构建与评价

目的 建立及评价西南地区婴幼儿(0～3岁)潮气呼吸肺功能预计公式.方法 采用德国JAEGER体积描记肺功能仪(MasterScreenTM Babybody plethysmograph),对来自西南地区166名0～3岁婴幼儿,进行潮气呼吸肺功能共12个指标的测定.通过SPSS18.0统计软件对数据进行处理,首先对各个肺功能指标与肺功能影响因素作相关性分析.二分类资料对各个肺功能指标的影响通过￡检验进行分析.各个肺功能指标作为方程的因变量,以性别(S)、年龄(A)、身长(L)、体质量(W)为自变量,作多元逐步回归,建立线性多元回归方程.将本研究构建的方程与国外文献中相应年龄范围的正常婴幼儿各个肺功能指标的方程进行比较,采用相对预测误差的平均值(％)=∑|预计值-实测值|/预计值×100％/n,评价各预计公式的优劣.结果 肺功能指标与年龄、体质量、身长及性别相关,婴幼儿呼吸频率与年龄呈直线负相关(r=-0.5),RR=30.30-0.06×A;婴幼儿潮气量与年龄呈直线正相关(r=0.8),VT =20.80+0.34 ×A +4.38 ×W +4.91 ×S;峰流速与潮气量的比与年龄呈直线负相关(r=-0.4),PTEF/VT=130.72-0.27 ×A-8.98 ×S;流速指标均与体质量呈直线正相关(r=0.5,0.5,0.6,0.6),PEF=25.62+ 6.88×W;TEF75=20.33+ 6.99×W;TEF50=23.07+6.39×W;TEF25=0.07+ 2.57×W +0.55 ×L.预计公式模型比较后显示,构建的预计模型及Nguyen TT的模型对本研究群体有较好的适用性.结论 建立的婴幼儿各项潮气呼吸肺功能指标的预计公式适用于中国西南地区3岁以下儿童的相关指标的预测.潮气呼吸肺功能指标与年龄和/或体质量呈线性相关.     

Genetic and Environmental Influences on Schizotypy among Adolescents in Taiwan: A Multivariate Twin/sibling Analysis

This study aimed to examine the relative contribution of genes and environment to psychometrically measured schizotypy and the causes for the covariation between different dimensions of schizotypy in a total of 330 pairs of twins and 36 same-sex sib-pairs aged 12–16 and systematically recruited from junior high schools in Taipei. Twins’ zygosity was determined by a combination of DNA typing and physical similarity. Schizotypy was measured using the Perceptual Aberration Scale (PAS) as well as the Schizotypal Personality Questionnaire (SPQ) and its three factors (Cognitive-perceptual Dysfunction, Disorganization, and Interpersonal Dysfunction). Univariate analyses of structural equation modeling using Mx program showed that scores on these schizotypal measures were substantially heritable (h ² ranging from 41 to 49%), with some genetic effects being non-additive. Multivariate analyses revealed common genetic factors linking between various traits of schizotypy, with bivariate heritability ranging from 50 to 65%. The proportion of the genetic contributions not shared with the other measures of schizotypy ranged from 24% for the Disorganization to 49% for the PAS scores. We concluded that there exist both common and specific genetic factors between the various dimensions of schizotypy, and at least half of their correlations were genetic in nature. Edited by Peter McGuffin     

国民社会健康测度及其影响因素研究——基于MIMIC结构方程模型的经验证据

为测量当前我国国民的社会健康水平,本文基于2012年CGSS数据,使用因子分析法得到的简单均值为3.13,因子加权均值为8.03。其中上海、天津、北京、辽宁、浙江、广东等经济发达省市的居民社会健康位列倒数1~6名,显示出我国居民社会健康与经济发展水平之间呈现相反的背离趋势。进一步使用MIMIC模型的估计结果表明,男性、已婚、年长、有医疗保险或养老保险、参加过社会捐赠或志愿活动的被访者具有更高水平的社会健康,社会地位、幸福感对社会健康具有显著的正向影响,城镇户籍、上网越多,其社会健康水平更低,教育、收入与社会健康之间呈倒U型关系。本文还利用2014年CLDS数据进行了复制研究,结果显示上述结论是稳健的,基于上述研究,本文提出了发展我国国民社会健康的政策建议。     

A multi-group path analysis of the relationship between perceived racial discrimination and self-rated stress: how does it vary across racial/ethnic groups?

Objective: The objective of this study was to answer three questions: (1) Is perceived discrimination adversely related to self-rated stress via the social capital and health care system distrust pathways? (2) Does the relationship between perceived discrimination and self-rated stress vary across race/ethnicity groups? and (3) Do the two pathways differ by one’s race/ethnicity background?

Design: Using the Philadelphia Health Management Corporation’s Southeastern Pennsylvania Household Survey, we classified 9831 respondents into 4 race/ethnicity groups: non-Hispanic White (n?=?6621), non-Hispanic Black (n?=?2359), Hispanic (n?=?505), and non-Hispanic other races (n?=?346). Structural equation modeling was employed to simultaneously estimate five sets of equations, including the confirmatory factor analysis for both social capital and health care distrust and both direct and indirect effects from perceived discrimination to self-rated stress.

Results: The key findings drawn from the analysis include the following: (1) in general, people who experienced racial discrimination have higher distrust and weaker social capital than those without perceived discrimination and both distrust and social capital are ultimately related to self-rated stress. (2) The direct relationship between perceived discrimination and self-rated stress is found for all race/ethnicity groups (except non-Hispanic other races) and it does not vary across groups. (3) The two pathways can be applied to non-Hispanic White and Black, but for Hispanic and non-Hispanic other races, we found little evidence for the social capital pathway.

Conclusions: For non-Hispanic White, non-Hispanic Black, and Hispanic, perceived discrimination is negatively related to self-rated stress. This finding highlights the importance of reducing interpersonal discriminatory behavior even for non-Hispanic White. The health care system distrust pathway can be used to address the racial health disparity in stress as it holds true for all four race/ethnicity groups. On the other hand, the social capital pathway seems to better help non-Hispanic White and Black to mediate the adverse effect of perceived discrimination on stress.     

Schwartz公式计算所得的儿童肾小球滤过率和万古霉素理想剂量之间的相关性研究

摘 要 目的：了解Schwartz公式计算所得到的儿童肾小球滤过率（GFR）和万古霉素理想剂量之间的关联性。评估是否可以通过GFR估算出万古霉素的理想剂量。方法: 通过治疗药物监测,计算出患儿万古霉素的清除率和理想日剂量。依据患儿血肌酐值,以Schwartz公式计算所得GFR值,观察不同GFR值范围内相对应的万古霉素日剂量分布,并采用线性回归分析法,评价GFR和上述药动学参数的相关性。结果: GFR值超过200 ml·min-1·(1.73m2)-1时,72.22%的患者理想剂量＞60 mg·kg-1·d-1;线性回归分析结果, GFR对于清除率和理想日剂量之间存在统计学意义上的线性关系,但其回归系数R2分别仅为0.307和0.361,GFR和理想日剂量之间的回归方程为Y=0.169X+29.577。结论: 由Schwartz公式所得的GFR值和万古霉素理想日剂量之间存在着统计学意义上的的线性关系,但是其相关性强度不高,无法用来准确估算万古霉素剂量。     

Secondary outcome analysis for data from an outcome‐dependent sampling design

Outcome‐dependent sampling (ODS) scheme is a cost‐effective way to conduct a study. For a study with continuous primary outcome, an ODS scheme can be implemented where the expensive exposure is only measured on a simple random sample and supplemental samples selected from 2 tails of the primary outcome variable. With the tremendous cost invested in collecting the primary exposure information, investigators often would like to use the available data to study the relationship between a secondary outcome and the obtained exposure variable. This is referred as secondary analysis. Secondary analysis in ODS designs can be tricky, as the ODS sample is not a random sample from the general population. In this article, we use the inverse probability weighted and augmented inverse probability weighted estimating equations to analyze the secondary outcome for data obtained from the ODS design. We do not make any parametric assumptions on the primary and secondary outcome and only specify the form of the regression mean models, thus allow an arbitrary error distribution. Our approach is robust to second‐ and higher‐order moment misspecification. It also leads to more precise estimates of the parameters by effectively using all the available participants. Through simulation studies, we show that the proposed estimator is consistent and asymptotically normal. Data from the Collaborative Perinatal Project are analyzed to illustrate our method.     

A Bayesian confirmatory factor model for multivariate observations in the form of two‐way tables of data

Researchers collected multiple measurements on patients with schizophrenia and their relatives, as well as control subjects and their relatives, to study vulnerability factors for schizophrenics and their near relatives. Observations across individuals from the same family are correlated, and also the multiple outcome measures on the same individuals are correlated. Traditional data analyses model outcomes separately and thus do not provide information about the interrelationships among outcomes. We propose a novel Bayesian family factor model (BFFM), which extends the classical confirmatory factor analysis model to explain the correlations among observed variables using a combination of family‐member and outcome factors. Traditional methods for fitting confirmatory factor analysis models, such as full‐information maximum likelihood (FIML) estimation using quasi‐Newton optimization (QNO), can have convergence problems and Heywood cases (lack of convergence) caused by empirical underidentification. In contrast, modern Bayesian Markov chain Monte Carlo handles these inference problems easily. Simulations compare the BFFM to FIML‐QNO in settings where the true covariance matrix is identified, close to not identified, and not identified. For these settings, FIML‐QNO fails to fit the data in 13%, 57%, and 85% of the cases, respectively, while MCMC provides stable estimates. When both methods successfully fit the data, estimates from the BFFM have smaller variances and comparable mean‐squared errors. We illustrate the BFFM by analyzing data on data from schizophrenics and their family members.     

Modeling individualized coefficient alpha to measure quality of test score data

Individualized coefficient alpha is defined. It is item and subject specific and is used to measure the quality of test score data with heterogenicity among the subjects and items. A regression model is developed based on 3 sets of generalized estimating equations. The first set of generalized estimating equation models the expectation of the responses, the second set models the response's variance, and the third set is proposed to estimate the individualized coefficient alpha, defined and used to measure individualized internal consistency of the responses. We also use different techniques to extend our method to handle missing data. Asymptotic property of the estimators is discussed, based on which inference on the coefficient alpha is derived. Performance of our method is evaluated through simulation study and real data analysis. The real data application is from a health literacy study in Hunan province of China.