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Prediction Intervals01:03

Prediction Intervals

2.6K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.6K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.6K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

143
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
143
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

322
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
322
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

5.1K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
5.1K
Confidence Intervals01:21

Confidence Intervals

9.0K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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Related Experiment Video

Updated: Nov 12, 2025

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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Validation of PARX Models for Default Count Prediction.

Arianna Agosto1, Emanuela Raffinetti2

  • 1Department of Economics and Management, University of Pavia, Pavia, Italy.

Frontiers in Artificial Intelligence
|March 18, 2021
PubMed
Summary
This summary is machine-generated.

Financial technology platforms require credit risk models that account for contagion. This study applies Poisson autoregressive models to default data, showing that incorporating contagion significantly improves predictive accuracy for financial stability.

Keywords:
PARX modelscontagioncredit risksystemic riskvalidation measures

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Area of Science:

  • Financial Mathematics
  • Econometrics
  • Risk Management

Background:

  • The interconnected nature of financial technology (FinTech) platforms necessitates advanced credit risk measurement models.
  • Evaluating the predictive accuracy of these models is crucial for investor protection and overall financial stability.

Purpose of the Study:

  • To apply Poisson autoregressive stochastic processes to default data for investigating credit contagion.
  • To assess the predictive accuracy of these models using standard metrics and a novel criterion extended for discrete data.

Main Methods:

  • Application of Poisson autoregressive models with exogenous covariates (PARX) to quarterly default counts.
  • Utilizing standard predictive accuracy metrics and a new criterion robust to variable type.

Main Results:

  • The study extends a new predictive accuracy criterion to discrete data, yielding results consistent with classical measures.
  • Poisson autoregressive models incorporating a contagion component demonstrated a decisive improvement in accuracy compared to autoregressive models alone.

Conclusions:

  • The integration of contagion effects into credit risk models is vital for accurately assessing default probabilities in interconnected financial systems.
  • The proposed methodology, using PARX models, offers a robust framework for evaluating credit contagion and enhancing model performance in FinTech contexts.