The objective of this study is to determine the role that obesity plays in how often Canadians visit their family doctors or general practitioners. Doctor visits are analyzed using mixtures of ordered probability mode...The objective of this study is to determine the role that obesity plays in how often Canadians visit their family doctors or general practitioners. Doctor visits are analyzed using mixtures of ordered probability models applied to sample survey data from the 2010 Canadian Community Health Survey. This procedure is shown to be superior in terms of likelihood criteria to the more usual one involving count models of doctor visits. The main result is that obesity is one of the leading causes of doctor visits. Obesity has become more important in the demand for physician services than smoking for all Canadians. Other factors including diabetes, the individual’s level of education, position in the income distribution, and drinking behavior are also important. The application of latent class’s ordered probability models by age-group and gender leads to results which are different from what others have found. While obesity is shown to be a serious problem in Canada, it has not yet reached the stage which some researchers have described as critical.展开更多
本文在无金标准情况下探讨皮肤毛孔标准照片制定的合理性和可行性,对医师诊断正确性进行评价。按照毛孔粗大程度制定分类为5水平的毛孔标准照片。对128名女性志愿者制作鼻翼毛孔照片,5位年资相近的皮肤科医师按照诊断标准和标准照片对12...本文在无金标准情况下探讨皮肤毛孔标准照片制定的合理性和可行性,对医师诊断正确性进行评价。按照毛孔粗大程度制定分类为5水平的毛孔标准照片。对128名女性志愿者制作鼻翼毛孔照片,5位年资相近的皮肤科医师按照诊断标准和标准照片对128例自愿者照片进行独立的等级评分。诊断结果数据采用潜在分类变量模型(Latent Class Model,LCM)进行分析,分别拟合5位医师诊断条件概率一致的模型和诊断条件概率不一致的模型。计算医师诊断的条件概率和后验概率。潜变量分析结果提示诊断标准过于细化且分类模糊,依据条件概率将原始分类重新划分为3类的模型较好地拟合了诊断数据。运用客观和准确的能够真实反应和区分个体情况的诊断标准是诊断试验评价的基础和前提。潜在分类模型能够有效地处理无金标准的诊断重复性或一致性研究数据。展开更多
确立科学合理的生态标准是建立和提高社会主义生态文明的关键。本文对当前我国运用选择实验法对农田生态补偿标准估算多假设受访者同质性偏好的现状,运用潜在分类模型(Latent Class Model,LCM)研究武汉市不同类别特征的市民对农田生态...确立科学合理的生态标准是建立和提高社会主义生态文明的关键。本文对当前我国运用选择实验法对农田生态补偿标准估算多假设受访者同质性偏好的现状,运用潜在分类模型(Latent Class Model,LCM)研究武汉市不同类别特征的市民对农田生态服务价值的保有意愿情况,并进一步运用Gold Latent软件测算基于市民支付意愿的武汉市农田生态补偿标准。研究发现:193.5%的武汉市受访市民愿意为保有和改善农田生态系统的服务功能进行一定额度的农田生态补偿标准支付,但仍有部分比例的受访者对农田生态服务价值的全面认知还不够(如空气质量)。2武汉市不同受访市民对农田生态属性的偏好存在异质性,93%的受访市民属于正常偏好型。另有7%的受访市民属于空气质量偏好型。受访者的收入水平、对农田生态服务价值的意识程度和是否愿意进行一定额度的农田生态补偿支付三个变量是影响其类别归属的决定性因素。3正常偏好型和空气质量偏好型市民对于农田生态补偿的支付意愿分别为6 888.74元/hm^2和518.50元/hm^2,计算得到基于武汉市市民平均支付意愿的农田生态补偿标准为7 407.24元/hm^2。研究结果不仅可以为武汉市农田保护制定出更具针对性和合理的农田生态补偿标准提供初步的科学依据,还可以为农田生态补偿筹集到除了中央政府财政转移之外的来自于农田生态服务受益者的资金支付。展开更多
目的比较删除法(deletion methods,DM)、基于对数线性模型的多重填补法(multiple imputation of category variables using log-linear model,M ILL)及基于潜在类别模型的多重填补法(multiple imputation based on latent class model,M...目的比较删除法(deletion methods,DM)、基于对数线性模型的多重填补法(multiple imputation of category variables using log-linear model,M ILL)及基于潜在类别模型的多重填补法(multiple imputation based on latent class model,M ILC)处理分类变量缺失数据的效果,并将M ILC应用于实例数据的分析。方法利用R语言产生不同缺失机制、缺失率和样本含量的多变量缺失模拟数据,运用DM、MILL和MILC处理形成完整数据集并进行logistic回归分析,通过回归系数的偏倚、均方根误差、稳定度和标准误偏倚评价各方法的处理效果。结果模拟实验表明当缺失率为5%时,三种方法处理效果均较好;随着缺失率的增大,MILL和MILC的各项评价指标均优于DM,且MILC的准确度高于MILL。三种方法处理效果均表现为完全随机缺失优于随机缺失、样本含量1000优于样本含量500。应用MILC对实例数据填补后标准误减小,回归系数估计更准确。结论本文应用MILL和MILC两种多重填补方法处理分类变量缺失数据均可减少缺失导致的参数估计偏倚。当缺失率>5%、样本含量1000时,建议应用MILC处理分类变量缺失数据。展开更多
文摘The objective of this study is to determine the role that obesity plays in how often Canadians visit their family doctors or general practitioners. Doctor visits are analyzed using mixtures of ordered probability models applied to sample survey data from the 2010 Canadian Community Health Survey. This procedure is shown to be superior in terms of likelihood criteria to the more usual one involving count models of doctor visits. The main result is that obesity is one of the leading causes of doctor visits. Obesity has become more important in the demand for physician services than smoking for all Canadians. Other factors including diabetes, the individual’s level of education, position in the income distribution, and drinking behavior are also important. The application of latent class’s ordered probability models by age-group and gender leads to results which are different from what others have found. While obesity is shown to be a serious problem in Canada, it has not yet reached the stage which some researchers have described as critical.
文摘本文在无金标准情况下探讨皮肤毛孔标准照片制定的合理性和可行性,对医师诊断正确性进行评价。按照毛孔粗大程度制定分类为5水平的毛孔标准照片。对128名女性志愿者制作鼻翼毛孔照片,5位年资相近的皮肤科医师按照诊断标准和标准照片对128例自愿者照片进行独立的等级评分。诊断结果数据采用潜在分类变量模型(Latent Class Model,LCM)进行分析,分别拟合5位医师诊断条件概率一致的模型和诊断条件概率不一致的模型。计算医师诊断的条件概率和后验概率。潜变量分析结果提示诊断标准过于细化且分类模糊,依据条件概率将原始分类重新划分为3类的模型较好地拟合了诊断数据。运用客观和准确的能够真实反应和区分个体情况的诊断标准是诊断试验评价的基础和前提。潜在分类模型能够有效地处理无金标准的诊断重复性或一致性研究数据。
文摘目的比较删除法(deletion methods,DM)、基于对数线性模型的多重填补法(multiple imputation of category variables using log-linear model,M ILL)及基于潜在类别模型的多重填补法(multiple imputation based on latent class model,M ILC)处理分类变量缺失数据的效果,并将M ILC应用于实例数据的分析。方法利用R语言产生不同缺失机制、缺失率和样本含量的多变量缺失模拟数据,运用DM、MILL和MILC处理形成完整数据集并进行logistic回归分析,通过回归系数的偏倚、均方根误差、稳定度和标准误偏倚评价各方法的处理效果。结果模拟实验表明当缺失率为5%时,三种方法处理效果均较好;随着缺失率的增大,MILL和MILC的各项评价指标均优于DM,且MILC的准确度高于MILL。三种方法处理效果均表现为完全随机缺失优于随机缺失、样本含量1000优于样本含量500。应用MILC对实例数据填补后标准误减小,回归系数估计更准确。结论本文应用MILL和MILC两种多重填补方法处理分类变量缺失数据均可减少缺失导致的参数估计偏倚。当缺失率>5%、样本含量1000时,建议应用MILC处理分类变量缺失数据。
