The unit root can lead to major problems in economic time series analyses. I obtain the asymptotic distributions of the ordinary least squares (OLS) estimator when the true model is trend stationary for the following ...The unit root can lead to major problems in economic time series analyses. I obtain the asymptotic distributions of the ordinary least squares (OLS) estimator when the true model is trend stationary for the following three cases: 1) the null model is a random walk without drift, and the auxiliary regression model does not contain a constant;2) the null model is a random walk with drift, and the auxiliary regression model contains a constant;and 3) the null model is a random walk with drift, and the auxiliary regression model contains both a constant and a time trend. In the third case, the asymptotic distribution of the OLS estimator is determined by the first order of the autocorrelation, and we can distinguish between the random walk and trend stationary models, unlike in previous studies. Based on these results, the real US gross domestic product is analyzed. A time trend model with autoregressive error terms is chosen. The results suggest that the impacts of a shock can become larger than the original shock in some periods and then gradually decline. However, the impacts continue for a long period, and policy makers should account for this to design better economic policies.展开更多
Background: Stroke is a worldwide health problem, the world’s second-leading cause of death and third-leading cause of disability. Currently, the majority of stroke patients are ischemic stroke patients. It is necess...Background: Stroke is a worldwide health problem, the world’s second-leading cause of death and third-leading cause of disability. Currently, the majority of stroke patients are ischemic stroke patients. It is necessary to evaluate risk factors to prevent ischemic stroke. Data and Methods: The risk factors for stroke in the previous fiscal year were analyzed. They were divided into nonmodifiable and modifiable factors. The probit and ordered probit models were used in the study, with 59341 and 50542 observations used in the estimation of the models, respectively. Results: Among the nonmodifiable factors, age, gender and cerebrovascular disease history are important risk factors. The history of cerebrovascular diseases is considered to be an especially important factor. Among the modifiable factors, taking antihypertensive drugs and recent large weight change are negative risk factors;however, sleeping well significantly reduces the probability of ischemic stroke. Conclusion: It is very important to ensure that medical personnel know a patient’s history of cerebrovascular diseases for proper treatments. Ischemic stroke might be considered an important side effect of antihypertensive drugs. Limitations: The dataset was observatory. There are various types of antihypertension drugs, and their effects are not analyzed.展开更多
Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regio...Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regions is basic but important. If the model contains stochastic factors such as random observation errors, determining the boundary is not easy. When the probability distributions are mis-specified, ordinal methods such as probit and logit maximum likelihood estimators (MLE) have large biases. The grouping estimator is a semiparametric estimator based on the grouping of data that does not require specific probability distributions. For 2D images, the grouping is simple. Monte Carlo experiments show that the grouping estimator clearly improves the probit MLE in many cases. The grouping estimator essentially makes the resolution density lower, and the present findings imply that methods using low-resolution image analyses might not be the proper ones in high-density image analyses. It is necessary to combine and compare the results of high- and low-resolution image analyses. The grouping estimator may provide theoretical justifications for such analysis.展开更多
文摘The unit root can lead to major problems in economic time series analyses. I obtain the asymptotic distributions of the ordinary least squares (OLS) estimator when the true model is trend stationary for the following three cases: 1) the null model is a random walk without drift, and the auxiliary regression model does not contain a constant;2) the null model is a random walk with drift, and the auxiliary regression model contains a constant;and 3) the null model is a random walk with drift, and the auxiliary regression model contains both a constant and a time trend. In the third case, the asymptotic distribution of the OLS estimator is determined by the first order of the autocorrelation, and we can distinguish between the random walk and trend stationary models, unlike in previous studies. Based on these results, the real US gross domestic product is analyzed. A time trend model with autoregressive error terms is chosen. The results suggest that the impacts of a shock can become larger than the original shock in some periods and then gradually decline. However, the impacts continue for a long period, and policy makers should account for this to design better economic policies.
文摘Background: Stroke is a worldwide health problem, the world’s second-leading cause of death and third-leading cause of disability. Currently, the majority of stroke patients are ischemic stroke patients. It is necessary to evaluate risk factors to prevent ischemic stroke. Data and Methods: The risk factors for stroke in the previous fiscal year were analyzed. They were divided into nonmodifiable and modifiable factors. The probit and ordered probit models were used in the study, with 59341 and 50542 observations used in the estimation of the models, respectively. Results: Among the nonmodifiable factors, age, gender and cerebrovascular disease history are important risk factors. The history of cerebrovascular diseases is considered to be an especially important factor. Among the modifiable factors, taking antihypertensive drugs and recent large weight change are negative risk factors;however, sleeping well significantly reduces the probability of ischemic stroke. Conclusion: It is very important to ensure that medical personnel know a patient’s history of cerebrovascular diseases for proper treatments. Ischemic stroke might be considered an important side effect of antihypertensive drugs. Limitations: The dataset was observatory. There are various types of antihypertension drugs, and their effects are not analyzed.
文摘Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regions is basic but important. If the model contains stochastic factors such as random observation errors, determining the boundary is not easy. When the probability distributions are mis-specified, ordinal methods such as probit and logit maximum likelihood estimators (MLE) have large biases. The grouping estimator is a semiparametric estimator based on the grouping of data that does not require specific probability distributions. For 2D images, the grouping is simple. Monte Carlo experiments show that the grouping estimator clearly improves the probit MLE in many cases. The grouping estimator essentially makes the resolution density lower, and the present findings imply that methods using low-resolution image analyses might not be the proper ones in high-density image analyses. It is necessary to combine and compare the results of high- and low-resolution image analyses. The grouping estimator may provide theoretical justifications for such analysis.
