Categorizing a continuous predictor subject to measurement error
نویسندگان
چکیده
منابع مشابه
Optimal rotation control for a qubit subject to continuous measurement
In this article we analyze the optimal control strategy for rotating a monitored qubit from an initial pure state to an orthogonal state in minimum time. This strategy is described for two different cost functions of interest which do not have the usual regularity properties. Hence, as classically smooth cost functions may not exist, we interpret these functions as viscosity solutions to the op...
متن کاملFitting a Finite Mixture Distribution to a Variable Subject to Heteroscedastic Measurement Error
We consider the case where a latent variable X cannot be observed directly and instead a variable W X U with an heteroscedastic measurement error U is observed It is assumed that the distribution of the true variable X is a mixture of normals and a type of the EM algorithm is applied to nd approxi mate ML estimates of the distribution parameters of X
متن کاملMulti-level IRT with Measurement Error in the Predictor Variables
In this paper a two-level regression model is imposed on the ability parameters in an IRT model. The advantage of using latent rather than observed scores as dependent variables of a multi-level model is that this offers the possibility of separating the influence of item difficulty and ability level and modeling response variation and measurement error. Another advantage is that, contrary to o...
متن کاملCategorizing Web Information on Subject with Statistical Language Modeling
With the rapid growth of the available information on the Internet, it is more difficult for us to find the relevant information quickly on the Web. Text classification, one of the most useful web information processing tools, has been paid more and more attention recently. Instead of using traditional classification models, we apply n-gram language models to classify Chinese Web text informati...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Electronic Journal of Statistics
سال: 2018
ISSN: 1935-7524
DOI: 10.1214/18-ejs1489