Add to ORKGWe argue that robust statistics has multiple goals, which are not always aligned. Robust thinking grew out of data analysis and the realisation that empirical evidence is at times supported merely by one or a few observations. The paper examines the outgrowth from this criticism of the statistical method over the last few decade
Policy-makers face an uncertain world. One way of getting a handle on decision-making in such an env...
Robust procedures are most informative when their results are compared to those from classical proce...
In 1966 the population biologist Richard Levins gave a forceful and in?uential defence of a method c...
In lieu of an abstract, here is the entry\u27s first paragraph: Robust statistics are procedures tha...
Abstract: Robust methods are little applied (although much studied by statisticians). We monitor ver...
The article addresses the question of how robust methods of regression are against outliers in a giv...
Outliers are sample values that cause surprise in relation to the majority of the sample. This is no...
Robust statistics, as a concept, probably dates back to the prehistory of statistics. It has, howeve...
It is argued that a main aim of statistics is to produce statistical procedures which in this articl...
In the first part of the paper, we trace the development of robust statistics through its main contr...
Robust methods are little applied (although much studied by statisticians). We monitor very robust r...
Robust Statistics deals with a pressing problem in statistical applications: many classical statisti...
Robust statistical methods are designed to work well when classical assumptions, typically normality...
A preeminent expert in the field explores new and exciting methodologies in the ever-growing field o...
In today’s society, statistical techniques are being used widely in education, medicine, social scie...
Policy-makers face an uncertain world. One way of getting a handle on decision-making in such an env...
Robust procedures are most informative when their results are compared to those from classical proce...
In 1966 the population biologist Richard Levins gave a forceful and in?uential defence of a method c...
In lieu of an abstract, here is the entry\u27s first paragraph: Robust statistics are procedures tha...
Abstract: Robust methods are little applied (although much studied by statisticians). We monitor ver...
The article addresses the question of how robust methods of regression are against outliers in a giv...
Outliers are sample values that cause surprise in relation to the majority of the sample. This is no...
Robust statistics, as a concept, probably dates back to the prehistory of statistics. It has, howeve...
It is argued that a main aim of statistics is to produce statistical procedures which in this articl...
In the first part of the paper, we trace the development of robust statistics through its main contr...
Robust methods are little applied (although much studied by statisticians). We monitor very robust r...
Robust Statistics deals with a pressing problem in statistical applications: many classical statisti...
Robust statistical methods are designed to work well when classical assumptions, typically normality...
A preeminent expert in the field explores new and exciting methodologies in the ever-growing field o...
In today’s society, statistical techniques are being used widely in education, medicine, social scie...
Policy-makers face an uncertain world. One way of getting a handle on decision-making in such an env...
Robust procedures are most informative when their results are compared to those from classical proce...
In 1966 the population biologist Richard Levins gave a forceful and in?uential defence of a method c...