Seventy-eight patients underwent coronary angiography and submaximal tre-admill exercise test to evaluate the Q-Tc,Q-Tr and ΔQ-Tc criteria for predicting co-ronary artery disease(CAD).The sensitivity,specificity,pred...Seventy-eight patients underwent coronary angiography and submaximal tre-admill exercise test to evaluate the Q-Tc,Q-Tr and ΔQ-Tc criteria for predicting co-ronary artery disease(CAD).The sensitivity,specificity,predicting value and correctdiagnostic rate of the Q-Tc and Q-Tr criteria were 84,76,83,81 percent and 69,88,89,77 percent,respectively,which had no significant differences when compared with ST de-pression.The Q-Tc had higher specificity(94%)than that of ST depression but less sen-sitivity(58%).These criteria could reflect the severity of coronary artery disease identi-fied with coronary angiography.Therefore,these criteria are usefel to interpret the resultsof stress test.展开更多
Chaos theory has taught us that a system which has both nonlinearity and random input will most likely produce irregular data. If random errors are irregular data, then random error process will raise nonlinearity （K...Chaos theory has taught us that a system which has both nonlinearity and random input will most likely produce irregular data. If random errors are irregular data, then random error process will raise nonlinearity （Kantz and Schreiber （1997））. Tsai （1986） introduced a composite test for autocorrelation and heteroscedasticity in linear models with AR（1） errors. Liu （2003） introduced a composite test for correlation and heteroscedasticity in nonlinear models with DBL（p, 0, 1） errors. Therefore, the important problems in regression model axe detections of bilinearity, correlation and heteroscedasticity. In this article, the authors discuss more general case of nonlinear models with DBL（p, q, 1） random errors by score test. Several statistics for the test of bilinearity, correlation, and heteroscedasticity are obtained, and expressed in simple matrix formulas. The results of regression models with linear errors are extended to those with bilinear errors. The simulation study is carried out to investigate the powers of the test statistics. All results of this article extend and develop results of Tsai （1986）, Wei, et al （1995）, and Liu, et al （2003）.展开更多
文摘Seventy-eight patients underwent coronary angiography and submaximal tre-admill exercise test to evaluate the Q-Tc,Q-Tr and ΔQ-Tc criteria for predicting co-ronary artery disease(CAD).The sensitivity,specificity,predicting value and correctdiagnostic rate of the Q-Tc and Q-Tr criteria were 84,76,83,81 percent and 69,88,89,77 percent,respectively,which had no significant differences when compared with ST de-pression.The Q-Tc had higher specificity(94%)than that of ST depression but less sen-sitivity(58%).These criteria could reflect the severity of coronary artery disease identi-fied with coronary angiography.Therefore,these criteria are usefel to interpret the resultsof stress test.
文摘Chaos theory has taught us that a system which has both nonlinearity and random input will most likely produce irregular data. If random errors are irregular data, then random error process will raise nonlinearity （Kantz and Schreiber （1997））. Tsai （1986） introduced a composite test for autocorrelation and heteroscedasticity in linear models with AR（1） errors. Liu （2003） introduced a composite test for correlation and heteroscedasticity in nonlinear models with DBL（p, 0, 1） errors. Therefore, the important problems in regression model axe detections of bilinearity, correlation and heteroscedasticity. In this article, the authors discuss more general case of nonlinear models with DBL（p, q, 1） random errors by score test. Several statistics for the test of bilinearity, correlation, and heteroscedasticity are obtained, and expressed in simple matrix formulas. The results of regression models with linear errors are extended to those with bilinear errors. The simulation study is carried out to investigate the powers of the test statistics. All results of this article extend and develop results of Tsai （1986）, Wei, et al （1995）, and Liu, et al （2003）.
