Displaying Bivariate Data
Authors
Abstract:
Numerical techniques are too often designed to yield specific answers to rigidly defined questions. Graphical techniques are less confining. They aid in understanding the numerous relationships reflected in the data. They help reveal the existence of peculiar looking observations or subsets of the data. It is difficult to obtain similar information from numerical procedures. In this article, by a real data, some types of displaying bivariate data are introduced.
similar resources
Estimation of Count Data using Bivariate Negative Binomial Regression Models
Abstract Negative binomial regression model (NBR) is a popular approach for modeling overdispersed count data with covariates. Several parameterizations have been performed for NBR, and the two well-known models, negative binomial-1 regression model (NBR-1) and negative binomial-2 regression model (NBR-2), have been applied. Another parameterization of NBR is negative binomial-P regression mode...
full textVisualizing bivariate long-tailed data
Variables in large data sets in biology or e-commerce often have a head, made up of very frequent values and a long tail of ever rarer values. Models such as the Zipf or Zipf–Mandelbrot provide a good description. The problem we address here is the visualization of two such long-tailed variables, as one might see in a bivariate Zipf context. We introduce a copula plot to display the joint behav...
full textInference with bivariate truncated data.
In this paper we build on previous work for estimation of the bivariate distribution of the time variables T1 and T2 when they are observable only on the condition that one of the time variables, say T1, is greater than (left-truncation) or less than (right truncation) some observed time variable C1. In this paper, we introduce several results based on the Influence Curve (which we derive in th...
full textHotspot Detection with Bivariate Data 1 Hotspot Detection with Bivariate Data
The Upper Level Scan Statistic (ULS), its theory, design and implementation, and its extension to the bivariate data are discussed. We provide the ULS-Hotspot algorithm that obtains the response rates, maintains a list of connected components at each level of the rate function and yields the ULS tree. The tree is grown in the immediate successor list, which provides a computationally efficient ...
full textAdaptive Thinning for Bivariate Scattered Data
This paper studies adaptive thinning strategies for approximating a large set of scattered data by piecewise linear functions over triangulated subsets. Our strategies depend on both the locations of the data points in the plane, and the values of the sampled function at these points — adaptive thinning. All our thinning strategies remove data points one by one, so as to minimize an estimate of...
full textSieve Estimation with Bivariate Interval Censored Data
Bivariate interval censored data arises in many applications. However, both theoretical and computational investigations for this type of data are limited because of the complicated structure of bivariate censoring. In this paper, we propose a two-stage spline-based sieve estimator for the association between two event times with bivariate case 2 interval censored data. A smooth and explicit es...
full textMy Resources
Journal title
volume 18 issue 1
pages 59- 69
publication date 2013-09
By following a journal you will be notified via email when a new issue of this journal is published.
No Keywords
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023