REPORTSA brief account of Seminars/ projects /workshops/events happened in CUSAThttps://dyuthi.cusat.ac.in:443/xmlui/handle/purl/49102019-03-05T17:37:29Z2019-03-05T17:37:29ZTesting Isotropy and a related Random Walk problemProf. J. S. Rao Jammalamadakahttps://dyuthi.cusat.ac.in:443/xmlui/handle/purl/49192015-03-11T07:32:31Z2015-03-11T00:00:00ZTesting Isotropy and a related Random Walk problem
Prof. J. S. Rao Jammalamadaka
One comes across directions as the observations in a number of situations. The first inferential question that one should answer when dealing with such data is, “Are they isotropic or uniformly distributed?” The answer to this question goes back in history which we shall retrace a bit and provide an exact and approximate solution to this so-called “Pearson’s Random Walk” problem.
Technical Report
2015-03-11T00:00:00ZModel Diagnostics in the presence of measurement errorProf. Hira Lal Koulhttps://dyuthi.cusat.ac.in:443/xmlui/handle/purl/49182015-03-09T05:28:22Z2015-03-09T00:00:00ZModel Diagnostics in the presence of measurement error
Prof. Hira Lal Koul
The problem of using information available from one variable X to make inferenceabout another Y is classical in many physical and social sciences. In statistics this isoften done via regression analysis where mean response is used to model the data. Onestipulates the model Y = µ(X) +ɛ. Here µ(X) is the mean response at the predictor variable value X = x, and ɛ = Y - µ(X) is the error. In classical regression analysis, both (X; Y ) are observable and one then proceeds to make inference about the mean response function µ(X). In practice there are numerous examples where X is not available, but a variable Z is observed which provides an estimate of X. As an example, consider the herbicidestudy of Rudemo, et al. [3] in which a nominal measured amount Z of herbicide was applied to a plant but the actual amount absorbed by the plant X is unobservable.
As another example, from Wang [5], an epidemiologist studies the severity of a lung disease, Y , among the residents in a city in relation to the amount of certain air pollutants. The amount of the air pollutants Z can be measured at certain observation stations in the city, but the actual exposure of the residents to the pollutants, X, is unobservable and may vary randomly from the Z-values. In both cases X = Z+error: This is the so called Berkson measurement error model.In more classical measurement error model one observes an unbiased estimator W of X and stipulates the relation W = X + error: An example of this model occurs when assessing effect of nutrition X on a disease. Measuring nutrition intake precisely within 24 hours is almost impossible. There are many similar examples in agricultural or medical studies, see e.g., Carroll, Ruppert and Stefanski [1] and Fuller [2], , among others. In this talk we shall address the question of fitting a parametric model to the re-gression function µ(X) in the Berkson measurement error model: Y = µ(X) + ɛ; X = Z + η; where η and ɛ are random errors with E(ɛ) = 0, X and η are d-dimensional, and Z is the observable d-dimensional r.v.
2015-03-09T00:00:00Z