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SUMMARY:Simulation-Based Insights and Novel Criteria for Linear Regression
  Modeling
DTSTART;VALUE=DATE-TIME:20260401T140000Z
DTEND;VALUE=DATE-TIME:20260401T150000Z
DTSTAMP;VALUE=DATE-TIME:20260415T015800Z
UID:indico-event-2088@events.imath.kiev.ua
DESCRIPTION:Speakers: Igor  Mandel ( NJ\, USA)\n\nAbstract: Large numbers
  of datasets were simulated via sampling\, and regression modeling results
  were compared with known parameters—an analysis undertaken here for the
  first time at this scale.\n\nThe study demonstrates that the impact of mu
 lticollinearity on the quality of parameter estimates is far stronger than
  commonly assumed\, even at low or moderate correlations between predictor
 s.\n\nThe standard practice of assessing the significance of regression co
 efficients using t-statistics is compared with the actual precision of est
 imates relative to their true values\, and the results are critically exam
 ined. It is shown that t-statistics for regression parameters can often be
  misleading.\n\nTwo novel approaches for selecting the most effective vari
 ables are proposed: one based on the so-called reference matrix and the ot
 her on efficiency indicators. A combined use of these methods\, together w
 ith the analysis of each variable’s contribution to the determination\, 
 is recommended.\n\nThe practical value of these approaches is confirmed th
 rough extensive testing on both\n\nsimulated homogeneous and heterogeneous
  datasets\, as well as on a real-world example. The results contribute to 
 a more accurate understanding of regression properties\, model quality cha
 racteristics\, and effective strategies for identifying the most reliable 
 predictors. They provide practitioners with better analytical tools.\n\n 
 \n\nThe presentation is based on the paper: Igor Mandel and Stan Lipovetsk
 y. Rethinking Linear Regression: Simulation-Based  Insights and Novel Cri
 teria for Modeling. \n\nAppliedMath 2025\, 5(4)\,140. https://doi.org/10.3
 390/appliedmath5040140\n\nhttps://events.imath.kiev.ua/event/2088/
LOCATION:https://knu-ua.zoom.us/j/89643295643?pwd=eTBZZSt0d0thZzFyaUhDUFNG
 TVE3QT09  (ONLINE)
URL:https://events.imath.kiev.ua/event/2088/
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