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Related Concept Videos

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
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Residuals and Least-Squares Property01:11

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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chngpt: threshold regression model estimation and inference.

Youyi Fong1, Ying Huang2, Peter B Gilbert2

  • 1Department of Biostatistics, Bioinformatics and Epidemiology, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, USA, 1100 Fairview Ave N., Seattle, USA. yfong@fhcrc.org.

BMC Bioinformatics
|October 18, 2017
PubMed
Summary
This summary is machine-generated.

The R package chngpt offers tools for threshold regression models, simplifying the analysis of nonlinear relationships. This package enhances accessibility for researchers studying threshold effects in various fields.

Keywords:
Change pointJump-typeRegression kinkSegmented regression model

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Area of Science:

  • Statistics
  • Biostatistics

Background:

  • Threshold regression models analyze nonlinear relationships using change points or thresholds.
  • These models offer an interpretable approach to modeling complex data patterns.

Purpose of the Study:

  • Introduce the R package chngpt for threshold regression analysis.
  • Provide estimation and hypothesis testing for common threshold regression variants.

Main Methods:

  • The chngpt package implements procedures for four threshold regression models.
  • Monte Carlo studies assess the consistency and Type 1 error rates of the procedures.
  • The package allows adjustment for additional covariates not subjected to thresholding.

Main Results:

  • Demonstrated consistency of estimation procedures.
  • Validated Type 1 error rates of hypothesis testing procedures.
  • Illustrated practical applications using HIV-1 Mother-To-Child-Transmission immune response data.

Conclusions:

  • The chngpt package offers unique contributions to threshold regression software.
  • Enhances the accessibility of threshold regression models for practitioners.
  • Facilitates the modeling of threshold effects in diverse research areas.