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Research Highlight
Statistical procedures are called robust if they remain informative and efficient in the presence of outliers and other departures from typical model assumptions on the data. Ignoring unusual observations can play havoc with standard statistical methods and can also result in losing the valuable information gotten from unusual data points. Robust procedures prevent this. And these procedures are more important than ever since currently, data are often collected without following established experimental protocols. As a result, data may not represent a single well-defined population. Analyzing these data by non-robust methods may result in biased conclusions. To perform reliable and informative inference based on such a heterogeneous data set, we need statistical methods that can fit models and identify patterns, focusing on the dominant homogeneous subset of the data without being affected by structurally different small subgroups. Robust Statistics does exactly this. Some examples of applications are finding exceptional athletes (e.g. hockey players), detecting intrusion in computer networks and constructing reliable single nucleotide polymorphism (SNP) genotyping.
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Statistics PhD candidates Kenny Chiu and Naitong Chen were honoured with the...
Congratulations, to the 2024 awardee, Farhan Samir! Farhan is in his fourth year of a Ph.D. in computational linguistics. He holds an NSERC Postgraduate Scholarship and an NSERC Postdoctoral Fellowship for his future postdoc studies. His...
The CANSSI Prairies Regional Centre organized the CANSSI Prairies Workshop Series in Data Science in 2023, offering an excellent opportunity for individuals to enhance their...
We were very happy to read the Government of Canada’s recent announcement of new and renewed Canada Research Chairs (CRCs), especially because one of our professors, Natalia Nolde,...