In the image parameter estimation by the linear regression, it has very high degrees of freedom for the decision of regression coefficients, because the dimension of image vector is huge high. In this paper, we discuss its potential by the learning of the dense samples. For the learning process, we employed a sequential regression coefficient calculation algorithm and realize its calculation fo...

We study a regression problem where for some part of the data we observe both label variable (Y) and predictors (X), while other only are given. Such arises, example, when observations costly may require skilled human agent. When conditional expectation E[Y|X] is not exactly linear, one can consider best linear approximation to expectation, which be estimated consistently by least-square estima...

Consider an investigator who obtains noisy observations on a process and who wishes to learn about the actual sequence of states through which the process has passed. Suppose the investigator believes the process is adequately described by non-linear dynamic and measurement equations. However, he sees no way to justify the assignment of specific probabilistic properties to residual error terms....

The paper refers to the situation teaching basic statistics to students with little background in mathematics, especially no knowledge in differential calculus. Goal of the paper is to show that knowledge of basic linear algebra is sufficient to explain the problem and the solution of linear regression, and this in a closed form. This approach avoids black box teaching where students learn how ...

OBJECTIVE This study aimed to identify those comorbidities with greatest impact on patient-reported health status in patients with chronic obstructive pulmonary disease (COPD) and to develop a comorbidity index that reflects their combined impact. STUDY DESIGN AND SETTING We included 408 Swiss and Dutch primary care patients with COPD from the International Collaborative Effort on Chronic Obs...