Sufficient Dimension Reduction with Nonignorable Nonresponse

Key Points

• The study reveals that sufficient dimension reduction techniques can effectively address nonignorable nonresponse, enhancing inference.
• Key findings show improved statistical performance in variable selection with nonignorable nonresponse scenarios, highlighting better accuracy.
• Statistical analysis employs sufficient dimension reduction to tackle challenges posed by nonignorable nonresponse issues in datasets.
• Significance may lead to advancements in methods used for handling missing data in various fields, necessitating broader application.