# Advanced Statistical Methods and Models

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/advanced-statistical-methods-and-models/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the detection, impact, and handling of multicollinearity in regression analysis. It discusses methods for identifying outliers, robust estimation techniques, variance inflation factors, and the use of depth functions in analyzing data. The cluster also explores the relative importance of predictors, principal component analysis, and the application of these concepts to functional data. |
| Domain | Physical Sciences |
| Field | Mathematics |
| OpenAlex ID | t11871 |
| Works | 55 |

## Topic papers all

Showing 15 of 55.

- [Regression Shrinkage and Selection Via the Lasso](https://scholariq.org/papers/regression-shrinkage-and-selection-via-the-lasso/)
- [Regression Modeling Strategies](https://scholariq.org/papers/regression-modeling-strategies/)
- [Approximate is Better than “Exact” for Interval Estimation of Binomial Proportions](https://scholariq.org/papers/approximate-is-better-than-exact-for-interval-estimation-of-binomial-proportions/)
- [The Importance of the Normality Assumption in Large Public Health Data Sets](https://scholariq.org/papers/the-importance-of-the-normality-assumption-in-large-public-health-data-sets/)
- [A review of variable selection methods in Partial Least Squares Regression](https://scholariq.org/papers/a-review-of-variable-selection-methods-in-partial-least-squares-regression/)
- [Linear regression with censored data](https://scholariq.org/papers/linear-regression-with-censored-data/)
- [Approximate Is Better than "Exact" for Interval Estimation of Binomial Proportions](https://scholariq.org/papers/approximate-is-better-than-exact-for-interval-estimation-of-binomial-proportions-2/)
- [Subset Selection in Regression](https://scholariq.org/papers/subset-selection-in-regression/)
- [Discarding or downweighting high-noise variables in factor analytic models](https://scholariq.org/papers/discarding-or-downweighting-high-noise-variables-in-factor-analytic-models/)
- [A Framework for Robust Subspace Learning](https://scholariq.org/papers/a-framework-for-robust-subspace-learning/)
- [Extending the Linear Model with R](https://scholariq.org/papers/extending-the-linear-model-with-r/)
- [Statistics for Engineers and Scientists](https://scholariq.org/papers/statistics-for-engineers-and-scientists/)
- [Interpreting Parameters in the Logistic Regression Model with Random Effects](https://scholariq.org/papers/interpreting-parameters-in-the-logistic-regression-model-with-random-effects/)
- [Understanding and controlling rotations in factor analytic models](https://scholariq.org/papers/understanding-and-controlling-rotations-in-factor-analytic-models/)
- [Time Series Forecasting with Neural Networks: A Comparative Study Using the Air Line Data](https://scholariq.org/papers/time-series-forecasting-with-neural-networks-a-comparative-study-using-the-air/)

## Topic primary papers

Showing 15 of 27.

- [Regression Modeling Strategies](https://scholariq.org/papers/regression-modeling-strategies/)
- [The Importance of the Normality Assumption in Large Public Health Data Sets](https://scholariq.org/papers/the-importance-of-the-normality-assumption-in-large-public-health-data-sets/)
- [Subset Selection in Regression](https://scholariq.org/papers/subset-selection-in-regression/)
- [Discarding or downweighting high-noise variables in factor analytic models](https://scholariq.org/papers/discarding-or-downweighting-high-noise-variables-in-factor-analytic-models/)
- [A Framework for Robust Subspace Learning](https://scholariq.org/papers/a-framework-for-robust-subspace-learning/)
- [Statistics for Engineers and Scientists](https://scholariq.org/papers/statistics-for-engineers-and-scientists/)
- [Understanding and controlling rotations in factor analytic models](https://scholariq.org/papers/understanding-and-controlling-rotations-in-factor-analytic-models/)
- [Robust principal component analysis for computer vision](https://scholariq.org/papers/robust-principal-component-analysis-for-computer-vision/)
- [Logistic Regression](https://scholariq.org/papers/logistic-regression/)
- [Practical Regression and Anova using R](https://scholariq.org/papers/practical-regression-and-anova-using-r/)
- [Quantitative measures of robustness for multivariable systems](https://scholariq.org/papers/quantitative-measures-of-robustness-for-multivariable-systems/)
- [Regression Rank Scores and Regression Quantiles](https://scholariq.org/papers/regression-rank-scores-and-regression-quantiles/)
- [Tests of linear hypotheses based on regression rank scores](https://scholariq.org/papers/tests-of-linear-hypotheses-based-on-regression-rank-scores/)
- [Beyond the t-Test: Statistical Equivalence Testing](https://scholariq.org/papers/beyond-the-t-test-statistical-equivalence-testing/)
- [Regression Analysis for a Functional Response](https://scholariq.org/papers/regression-analysis-for-a-functional-response-2/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.