Medical research data are often skewed and heteroscedastic. It has therefore become practice to log-transform data in regression analysis, in order to stabilize the variance. Regression analysis on log-transformed dat...Medical research data are often skewed and heteroscedastic. It has therefore become practice to log-transform data in regression analysis, in order to stabilize the variance. Regression analysis on log-transformed data estimates the relative effect, whereas it is often the absolute effect of a predictor that is of interest. We propose a maximum likelihood (ML)-based approach to estimate a linear regression model on log-normal, heteroscedastic data. The new method was evaluated with a large simulation study. Log-normal observations were generated according to the simulation models and parameters were estimated using the new ML method, ordinary least-squares regression (LS) and weighed least-squares regression (WLS). All three methods produced unbiased estimates of parameters and expected response, and ML and WLS yielded smaller standard errors than LS. The approximate normality of the Wald statistic, used for tests of the ML estimates, in most situations produced correct type I error risk. Only ML and WLS produced correct confidence intervals for the estimated expected value. ML had the highest power for tests regarding β1.展开更多
The present paper proposes a new robust estimator for Poisson regression models. We used the weighted maximum likelihood estimators which are regarded as Mallows-type estimators. We perform a Monte Carlo simulation st...The present paper proposes a new robust estimator for Poisson regression models. We used the weighted maximum likelihood estimators which are regarded as Mallows-type estimators. We perform a Monte Carlo simulation study to assess the performance of a suggested estimator compared to the maximum likelihood estimator and some robust methods. The result shows that, in general, all robust methods in this paper perform better than the classical maximum likelihood estimators when the model contains outliers. The proposed estimators showed the best performance compared to other robust estimators.展开更多
Often the lifecycle data occur as count of the vital events and are recorded as integers.The purpose of this article is to model the fertility behavior based on religious,educational,economic,and occupational characte...Often the lifecycle data occur as count of the vital events and are recorded as integers.The purpose of this article is to model the fertility behavior based on religious,educational,economic,and occupational characteristics.The responses of classified groups according to these determinants are examined for significant influence on fertility using Poisson regression model(PRM) based on the National Family Health Survey-3 dataset.The observed and predicted probabilities under PRM indicate modal value of two children for the Poisson distribution modeled data.Presence of dominance of two child in the data motivates the authors to adopt multinomial regression model(MRM) in order to link fertility with various socioeconomic indicators responsible for fertility variation.Choice of the explanatory factors is limited to the availability of data.Trends and patterns of preference for birth counts suggest that religion,caste,wealth,female education,and occupation are the dominant factors shaping the observed birth process.Empirical analysis suggests that both the models used in the study perform similarly on the sample data.However,fitting of MRM by taking birth count of two as comparison category shows improved Akaike information criterion and consistent Akaike information criterion values.Current work contributes to the existing literature as it attempts to provide more insight into the determinants of Indian fertility using Poisson and MRM.展开更多
目的介绍应用修正poisson回归模型计算常见结局事件的前瞻性研究中暴露因素的调整相对危险度的精确区间估计值。方法应用稳健误差方差估计法(sandwich variance esti mator)来校正相对危险度(RR)的估计方差,并通过SAS程序中GENMOD过程的...目的介绍应用修正poisson回归模型计算常见结局事件的前瞻性研究中暴露因素的调整相对危险度的精确区间估计值。方法应用稳健误差方差估计法(sandwich variance esti mator)来校正相对危险度(RR)的估计方差,并通过SAS程序中GENMOD过程的REPEATED语句实现修正poisson回归。此外,采用不同的统计方法对5个虚拟的研究数据进行了分析比较。结果以分层的Mantel-Haenszel法为标准参照,修正poisson回归对aRR点和区间估计均较为理想,普通poisson回归的aRR区间估计偏于保守。而logistic回归得到的aOR值明显偏离真实的RR值。结论修正poisson回归模型适合于处理常见结局事件的前瞻性研究资料。展开更多
文摘Medical research data are often skewed and heteroscedastic. It has therefore become practice to log-transform data in regression analysis, in order to stabilize the variance. Regression analysis on log-transformed data estimates the relative effect, whereas it is often the absolute effect of a predictor that is of interest. We propose a maximum likelihood (ML)-based approach to estimate a linear regression model on log-normal, heteroscedastic data. The new method was evaluated with a large simulation study. Log-normal observations were generated according to the simulation models and parameters were estimated using the new ML method, ordinary least-squares regression (LS) and weighed least-squares regression (WLS). All three methods produced unbiased estimates of parameters and expected response, and ML and WLS yielded smaller standard errors than LS. The approximate normality of the Wald statistic, used for tests of the ML estimates, in most situations produced correct type I error risk. Only ML and WLS produced correct confidence intervals for the estimated expected value. ML had the highest power for tests regarding β1.
文摘The present paper proposes a new robust estimator for Poisson regression models. We used the weighted maximum likelihood estimators which are regarded as Mallows-type estimators. We perform a Monte Carlo simulation study to assess the performance of a suggested estimator compared to the maximum likelihood estimator and some robust methods. The result shows that, in general, all robust methods in this paper perform better than the classical maximum likelihood estimators when the model contains outliers. The proposed estimators showed the best performance compared to other robust estimators.
基金supported by R&D Grant from University of DelhiDU-DST PURSE GrantICMR Grant No.3/1/3/JRF-2010/HRD-122(35831)
文摘Often the lifecycle data occur as count of the vital events and are recorded as integers.The purpose of this article is to model the fertility behavior based on religious,educational,economic,and occupational characteristics.The responses of classified groups according to these determinants are examined for significant influence on fertility using Poisson regression model(PRM) based on the National Family Health Survey-3 dataset.The observed and predicted probabilities under PRM indicate modal value of two children for the Poisson distribution modeled data.Presence of dominance of two child in the data motivates the authors to adopt multinomial regression model(MRM) in order to link fertility with various socioeconomic indicators responsible for fertility variation.Choice of the explanatory factors is limited to the availability of data.Trends and patterns of preference for birth counts suggest that religion,caste,wealth,female education,and occupation are the dominant factors shaping the observed birth process.Empirical analysis suggests that both the models used in the study perform similarly on the sample data.However,fitting of MRM by taking birth count of two as comparison category shows improved Akaike information criterion and consistent Akaike information criterion values.Current work contributes to the existing literature as it attempts to provide more insight into the determinants of Indian fertility using Poisson and MRM.
文摘目的介绍应用修正poisson回归模型计算常见结局事件的前瞻性研究中暴露因素的调整相对危险度的精确区间估计值。方法应用稳健误差方差估计法(sandwich variance esti mator)来校正相对危险度(RR)的估计方差,并通过SAS程序中GENMOD过程的REPEATED语句实现修正poisson回归。此外,采用不同的统计方法对5个虚拟的研究数据进行了分析比较。结果以分层的Mantel-Haenszel法为标准参照,修正poisson回归对aRR点和区间估计均较为理想,普通poisson回归的aRR区间估计偏于保守。而logistic回归得到的aOR值明显偏离真实的RR值。结论修正poisson回归模型适合于处理常见结局事件的前瞻性研究资料。
