PNL23 CONTROLLING SELECTION BIAS ON CONTINUOUS VARIABLES
نویسندگان
چکیده
منابع مشابه
Controlling Selection Bias in Causal Inference
Selection bias, caused by preferential exclusion of samples from the data, is a major obstacle to valid causal and statistical inferences; it cannot be removed by randomized experiments and can hardly be detected in either experimental or observational studies. This paper highlights several graphical and algebraic methods capable of mitigating and sometimes eliminating this bias. These nonparam...
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Controlling selection bias, a statistical error caused by preferential sampling of data, is a fundamental problem in machine learning and statistical inference. This paper presents a simple criterion for controlling selection bias in the odds ratio, a widely used measure for association between variables, that connects the nature of selection bias with the graph modeling the selection mechanism...
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In a wide variety of organisms, synonymous codons are used with different frequencies, a phenomenon known as codon bias. Population genetic studies have shown that synonymous sites are under weak selection and that codon bias is maintained by a balance between selection, mutation, and genetic drift. It appears that the major cause for selection on codon bias is that certain preferred codons are...
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Continuous random variables are random quantities that are measured on a continuous scale. They can usually take on any value over some interval, which distinguishes them from discrete random variables, which can take on only a sequence of values, usually integers. Typically random variables that represent, for example, time or distance will be continuous rather than discrete. Just as we descri...
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ژورنال
عنوان ژورنال: Value in Health
سال: 2006
ISSN: 1098-3015
DOI: 10.1016/s1098-3015(10)64572-x