One of the most commonly used statistical methods is bivariate correlation analysis. However, it is usually the case that little or no attention is given to power and sample size considerations when planning a study i...One of the most commonly used statistical methods is bivariate correlation analysis. However, it is usually the case that little or no attention is given to power and sample size considerations when planning a study in which correlation will be the primary analysis. In fact, when we reviewed studies published in clinical research journals in 2014, we found that none of the 111 articles that presented results of correlation analyses included a sample size justification. It is sometimes of interest to compare two correlation coefficients between independent groups. For example, one may wish to compare diabetics and non-diabetics in terms of the correlation of systolic blood pressure with age. Tools for performing power and sample size calculations for the comparison of two independent Pearson correlation coefficients are widely available;however, we were unable to identify any easily accessible tools for power and sample size calculations when comparing two independent Spearman rank correlation coefficients or two independent Kendall coefficients of concordance. In this article, we provide formulas and charts that can be used to calculate the sample size that is needed when testing the hypothesis that two independent Spearman or Kendall coefficients are equal.展开更多
Heteroscedasticity and multicollinearity are serious problems when they exist in econometrics data. These problems exist as a result of violating the assumptions of equal variance between the error terms and that of i...Heteroscedasticity and multicollinearity are serious problems when they exist in econometrics data. These problems exist as a result of violating the assumptions of equal variance between the error terms and that of independence between the explanatory variables of the model. With these assumption violations, Ordinary Least Square Estimator</span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">(OLS) will not give best linear unbiased, efficient and consistent estimator. In practice, there are several structures of heteroscedasticity and several methods of heteroscedasticity detection. For better estimation result, best heteroscedasticity detection methods must be determined for any structure of heteroscedasticity in the presence of multicollinearity between the explanatory variables of the model. In this paper we examine the effects of multicollinearity on type I error rates of some methods of heteroscedasticity detection in linear regression model in other to determine the best method of heteroscedasticity detection to use when both problems exist in the model. Nine heteroscedasticity detection methods were considered with seven heteroscedasticity structures. Simulation study was done via a Monte Carlo experiment on a multiple linear regression model with 3 explanatory variables. This experiment was conducted 1000 times with linear model parameters of </span><span style="white-space:nowrap;"><em><span style="font-family:Verdana;">β</span></em><sub><span style="font-family:Verdana;">0</span></sub><span style="font-family:Verdana;"> = 4 , </span><em><span style="font-family:Verdana;">β</span></em><sub><span style="font-family:Verdana;">1</span></sub><span style="font-family:Verdana;"> = 0.4 , </span><em><span style="font-family:Verdana;">β</span></em><sub><span style="font-family:Verdana;">2</span></sub><span style="font-family:Verdana;">= 1.5</span></span></span><span style="font-family:""><span style="font-family:Verdana;"> and </span><em style="font-family:""><span style="font-family:Verdana;">β</span><span style="font-family:Verdana;"><sub>3 </sub></span></em><span style="font-family:Verdana;">= 3.6</span><span style="font-family:Verdana;">. </span><span style="font-family:Verdana;">Five (5) </span><span style="font-family:Verdana;"></span><span style="font-family:Verdana;">levels of</span><span style="white-space:nowrap;font-family:Verdana;"> </span><span style="font-family:Verdana;"></span><span style="font-family:Verdana;">mulicollinearity </span></span><span style="font-family:Verdana;">are </span><span style="font-family:Verdana;">with seven</span><span style="font-family:""> </span><span style="font-family:Verdana;">(7) different sample sizes. The method’s performances were compared with the aids of set confidence interval (C.I</span><span style="font-family:Verdana;">.</span><span style="font-family:Verdana;">) criterion. Results showed that whenever multicollinearity exists in the model with any forms of heteroscedasticity structures, Breusch-Godfrey (BG) test is the best method to determine the existence of heteroscedasticity at all chosen levels of significance.展开更多
Objective To observe the alterations of serum thyroid hormone levels in acute exacerbation of chronic obstructive pulmonary disease(AECOPD)patients without thyroid disease and therefore to investigate the association ...Objective To observe the alterations of serum thyroid hormone levels in acute exacerbation of chronic obstructive pulmonary disease(AECOPD)patients without thyroid disease and therefore to investigate the association between serum thyroid hormone levels and the severity and prognosis of AECOPD.Methods Serum thyroid hormone levels including TT4,TT3,TSH,FT4 and展开更多
The discontinuities in historical Chinese radiosonde datasets are attributed to artificial errors. In order to reflect more realistically basic conditions of the atmosphere over China and provide more reasonable radio...The discontinuities in historical Chinese radiosonde datasets are attributed to artificial errors. In order to reflect more realistically basic conditions of the atmosphere over China and provide more reasonable radiosonde data as input to climate change analysis and to atmospheric reanalysis data assimilation systems, this paper proposes a scheme to identify breakpoints and adjust biases in daily radiosonde observations. The ongoing ECMWF Re Analysis-Interim(ERA-Interim) 12-h forecasts are used as reference series in the scheme, complemented by the ECMWF Twentieth Century Reanalysis(ERA-20 C). A series of breakpoint identification schemes are developed and combined with metadata to detect breakpoints. The Quantile-Matching(QM) method is applied to test and adjust daily radiosonde data on 12 mandatory pressure levels collected at 80 sounding stations during 1979–2013. The adjusted temperatures on mandatory levels are interpolated to significant levels for temperature adjustment on these levels. The adjustment scheme not only solves the data discontinuity problem caused by changes in observational instruments and bias correction methods, but also solves the discontinuity problem in the 1200 minus 0000 UTC temperature time series on mandatory levels at individual sounding stations. Before the adjustment, obvious discontinuities can be found in the deviation field between the raw radiosonde data and ERA-Interim reanalysis with relatively large deviations before 2001. The deviation discontinuity is mainly attributed to the nationwide upgrade of the radiosonde system in China around 2001. After the adjustment, the time series of deviations becomes more continuous. In addition, compared with the adjusted temperature data on mandatory levels over 80 radiosonde stations in China contained in the Radiosonde Observation Correction Using Reanalyses(RAOBCORE) 1.5, the dataset adjusted by the method proposed in the present study exhibits higher quality than RAOBCORE 1.5, while discontinuities still exist in the time series of temperature at 0000, 1200, and 1200 minus 0000 UTC in RAOBCORE 1.5.展开更多
文摘One of the most commonly used statistical methods is bivariate correlation analysis. However, it is usually the case that little or no attention is given to power and sample size considerations when planning a study in which correlation will be the primary analysis. In fact, when we reviewed studies published in clinical research journals in 2014, we found that none of the 111 articles that presented results of correlation analyses included a sample size justification. It is sometimes of interest to compare two correlation coefficients between independent groups. For example, one may wish to compare diabetics and non-diabetics in terms of the correlation of systolic blood pressure with age. Tools for performing power and sample size calculations for the comparison of two independent Pearson correlation coefficients are widely available;however, we were unable to identify any easily accessible tools for power and sample size calculations when comparing two independent Spearman rank correlation coefficients or two independent Kendall coefficients of concordance. In this article, we provide formulas and charts that can be used to calculate the sample size that is needed when testing the hypothesis that two independent Spearman or Kendall coefficients are equal.
文摘Heteroscedasticity and multicollinearity are serious problems when they exist in econometrics data. These problems exist as a result of violating the assumptions of equal variance between the error terms and that of independence between the explanatory variables of the model. With these assumption violations, Ordinary Least Square Estimator</span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">(OLS) will not give best linear unbiased, efficient and consistent estimator. In practice, there are several structures of heteroscedasticity and several methods of heteroscedasticity detection. For better estimation result, best heteroscedasticity detection methods must be determined for any structure of heteroscedasticity in the presence of multicollinearity between the explanatory variables of the model. In this paper we examine the effects of multicollinearity on type I error rates of some methods of heteroscedasticity detection in linear regression model in other to determine the best method of heteroscedasticity detection to use when both problems exist in the model. Nine heteroscedasticity detection methods were considered with seven heteroscedasticity structures. Simulation study was done via a Monte Carlo experiment on a multiple linear regression model with 3 explanatory variables. This experiment was conducted 1000 times with linear model parameters of </span><span style="white-space:nowrap;"><em><span style="font-family:Verdana;">β</span></em><sub><span style="font-family:Verdana;">0</span></sub><span style="font-family:Verdana;"> = 4 , </span><em><span style="font-family:Verdana;">β</span></em><sub><span style="font-family:Verdana;">1</span></sub><span style="font-family:Verdana;"> = 0.4 , </span><em><span style="font-family:Verdana;">β</span></em><sub><span style="font-family:Verdana;">2</span></sub><span style="font-family:Verdana;">= 1.5</span></span></span><span style="font-family:""><span style="font-family:Verdana;"> and </span><em style="font-family:""><span style="font-family:Verdana;">β</span><span style="font-family:Verdana;"><sub>3 </sub></span></em><span style="font-family:Verdana;">= 3.6</span><span style="font-family:Verdana;">. </span><span style="font-family:Verdana;">Five (5) </span><span style="font-family:Verdana;"></span><span style="font-family:Verdana;">levels of</span><span style="white-space:nowrap;font-family:Verdana;"> </span><span style="font-family:Verdana;"></span><span style="font-family:Verdana;">mulicollinearity </span></span><span style="font-family:Verdana;">are </span><span style="font-family:Verdana;">with seven</span><span style="font-family:""> </span><span style="font-family:Verdana;">(7) different sample sizes. The method’s performances were compared with the aids of set confidence interval (C.I</span><span style="font-family:Verdana;">.</span><span style="font-family:Verdana;">) criterion. Results showed that whenever multicollinearity exists in the model with any forms of heteroscedasticity structures, Breusch-Godfrey (BG) test is the best method to determine the existence of heteroscedasticity at all chosen levels of significance.
文摘Objective To observe the alterations of serum thyroid hormone levels in acute exacerbation of chronic obstructive pulmonary disease(AECOPD)patients without thyroid disease and therefore to investigate the association between serum thyroid hormone levels and the severity and prognosis of AECOPD.Methods Serum thyroid hormone levels including TT4,TT3,TSH,FT4 and
基金Supported by the National Innovation Project for Meteorological Science and Technology (CMAGGTD003-5)China Meteorological Administration Special Public Welfare Research Fund (GYHY201506002)National Key Research and Development Program of China (2017YFC1501801)。
文摘The discontinuities in historical Chinese radiosonde datasets are attributed to artificial errors. In order to reflect more realistically basic conditions of the atmosphere over China and provide more reasonable radiosonde data as input to climate change analysis and to atmospheric reanalysis data assimilation systems, this paper proposes a scheme to identify breakpoints and adjust biases in daily radiosonde observations. The ongoing ECMWF Re Analysis-Interim(ERA-Interim) 12-h forecasts are used as reference series in the scheme, complemented by the ECMWF Twentieth Century Reanalysis(ERA-20 C). A series of breakpoint identification schemes are developed and combined with metadata to detect breakpoints. The Quantile-Matching(QM) method is applied to test and adjust daily radiosonde data on 12 mandatory pressure levels collected at 80 sounding stations during 1979–2013. The adjusted temperatures on mandatory levels are interpolated to significant levels for temperature adjustment on these levels. The adjustment scheme not only solves the data discontinuity problem caused by changes in observational instruments and bias correction methods, but also solves the discontinuity problem in the 1200 minus 0000 UTC temperature time series on mandatory levels at individual sounding stations. Before the adjustment, obvious discontinuities can be found in the deviation field between the raw radiosonde data and ERA-Interim reanalysis with relatively large deviations before 2001. The deviation discontinuity is mainly attributed to the nationwide upgrade of the radiosonde system in China around 2001. After the adjustment, the time series of deviations becomes more continuous. In addition, compared with the adjusted temperature data on mandatory levels over 80 radiosonde stations in China contained in the Radiosonde Observation Correction Using Reanalyses(RAOBCORE) 1.5, the dataset adjusted by the method proposed in the present study exhibits higher quality than RAOBCORE 1.5, while discontinuities still exist in the time series of temperature at 0000, 1200, and 1200 minus 0000 UTC in RAOBCORE 1.5.
