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Let's say we have a cumulative distribution function (cdf) G and random numbers v1 and v2.
The definition of strict increasing function is: v1 < v2 => G(v1) < G(v2).
In a statistics book, the author writes:
"...but with the additional assumption that the cdf G is a strictly increasing function. That is, v1 < v2 <=> G(v1) < G(v2)".
a) He writes the definition with an equivalence (<=>) and not an implication (=>). Could someone explain why? Does in fact the definition also imply that its is an equivalence?
b) The authors definition: "v1 < v2 <=> G(v1) < G(v2)" must imply that:
v1 = v2 <=> G(v1) = G(v2). Correct?
Any help is very appreciated!
The definition of strict increasing function is: v1 < v2 => G(v1) < G(v2).
In a statistics book, the author writes:
"...but with the additional assumption that the cdf G is a strictly increasing function. That is, v1 < v2 <=> G(v1) < G(v2)".
a) He writes the definition with an equivalence (<=>) and not an implication (=>). Could someone explain why? Does in fact the definition also imply that its is an equivalence?
b) The authors definition: "v1 < v2 <=> G(v1) < G(v2)" must imply that:
v1 = v2 <=> G(v1) = G(v2). Correct?
Any help is very appreciated!
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