Definations:
For a random vector and a sequence of random vectors defined on a probability space .
1. Convergence in Probability or Convergence of probability measures, is kind of convergence in measure, in concept of the measure theory.
: for any fixed .
2. Convergence with Probability 1, is kind of pointwise convergence in real analysis, it is also called almost surely convergence in the measure theory.
: if or .
Convergence in Probability is weaker than Convergence with Probability 1, as stated below:
Theorem:
Proof: {: for each there exists an such that for all }
{{ there exists an such that for all }}
{{ for all }}
{{ }}
So
for all
for all
for all
for all , or for any fixed , that is .
if , let the set of unconvergence points as then .
if , let the set of unconvergence points as then we donot have .
Convergence in Probability do not require but release the condition to , where is a set which will go smaller as .
From the proof above we can see that and , that is to say is bigger than
References:
A Course in Large Sample Theory(Lecture notes), Xianyi Wu
A Course in Large Sample Theory, Thomas S. Ferguson
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