Parametric statistics

About: Parametric statistics is a research topic. Over the lifetime, 39200 publications have been published within this topic receiving 765761 citations.

A Theoretical Study of Performance of an Orthogonal Multiplexing Data Transmission Scheme

Abstract: This paper presents a theoretical analysis of the performance of an orthogonal multiplexing data transmission scheme (parallel transmission scheme) subject to a number of degrading factors normally encountered by a practical operating system. The factors considered jointly are sampling time error, carrier phase offset, and nonideal phase characteristics of transmitting and receiving filters. Performance is measured by the familiar criterion of eye opening of the received data signal. A closed-form expression for the eye opening is obtained. It is shown that the lengthy nonlinear functions in the solution can be closely approximated by simple piecewise linear functions for parameter values of interest. The optimum settings of the sampling time and the carrier phase are determined for given filter phase distortion. Also, considering all factors, simple formulas are developed for computing interchannel interferences, intersymbol interference, and the resulting eye opening. Simple relationships between the eye opening and filter phase distortion are explored, and a concept of parametric eye is introduced to aid in filter design. An illustrative example is considered which shows that the impairment of the system performance is not excessive for a reasonable range of system design parameters.

Event History Analysis With Stata

Abstract: Contents: Preface. Introduction. Event History Data Structures. Nonparametric Descriptive Methods. Exponential Transition Rate Models. Piecewise Constant Exponential Models. Exponential Models With Time-Dependent Covariates. Parametric Models of Time-Dependence. Methods to Check Parametric Assumptions. Semiparametric Transition Rate Models. Problems of Model Specification.

Statistics, Probability and Reliability for Civil and Environmental Engineers

Abstract: Preliminary data analysis basic concepts and definitions statistical properties of distributions probability models model estimation and testing regression and forecasting multivariate statistical methods extreme value analysis stochastic models of processes simulaton risk and reliability analysis reliability design decision methods mathematical addendum annotated references and related readings tables of parametric families of probability models annotated software for statistical data analysis

Diffusion tensor imaging.

Abstract: Diffusion tensor MRI (DT-MRI) is the only non-invasive method for characterising the microstructural organization of tissue in vivo. Generating parametric maps that help to visualise different aspects of the tissue microstructure (mean diffusivity, tissue anisotropy and dominant fibre orientation) involves a number of steps from deciding on the optimal acquisition parameters on the scanner, collecting the data, pre-processing the data and fitting the model to generating final parametric maps for entry into statistical data analysis. Here, we describe an entire protocol that we have used on over 400 subjects with great success in our laboratory. In the ‘Notes’ section, we justify our choice of the various parameters/choices along the way so that the reader may adapt/modify the protocol to their own time/hardware constraints.

Stochastic finite element: a non intrusive approach by regression

Abstract: The stochastic finite element method allows to solve stochastic boundary value problems where material properties and loads are random. The method is based on the expansion of the mechanical response onto the so-called polynomial chaos. In this paper, a non intrusive method based on a least-squares minimization procedure is presented. This method is illustrated by the study of the settlement of a foundation. Different analysis are proposed: the computation of the statistical moments of the response, a reliability analysis and a parametric sensitivity analysis.