Wald Theorem 2.2.1 Problem on p.15

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In summary, the conversation discusses a question about Theorem 2.2.1 in Wald's GR book. The speaker mentions that formula 2.2.5 is derived using 2.2.4, but there is a concern about the validity of 2.2.5 due to the restriction of ##f## only being defined in a specific domain. Additionally, there is confusion about the application of ##v## and ##x^{\mu}\circ\psi## in the formulas. The speaker then shares a theorem from another GR book that resolves their initial question.
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syhpehtam
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Hi, I have some trouble with Theorem 2.2.1 in Wald's GR book p.15.

He derived formula 2.2.5 by using 2.2.4. Here, the ##f## in ##v(f)## is a map ##f:M\rightarrow\mathbb R##, but 2.2.4 is the expression for ##f## only in the domain ##O\subseteq M## and we don't know the expression for ##f## outside ##O##. So how can 2.2.5 be valid. ##v## is a map ##v:\mathcal F\rightarrow\mathbb R##, but ##x^{\mu}\circ\psi## is a map:##O\rightarrow\mathbb R## which doesn't belong to##\mathcal F##, so ##v(x^{\mu}\circ\psi)## in 2.2.5 & 2.2.7 doesn't make sense.

Is there anything assumed in advance by the author that make these wrong expressions in the formulas become meaningful?
 
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I've found a theorem in another GR book which says:
If ##f_1,f_2\in\mathcal F##,and there exists a neighborhood ##N## of ##p\in M## such that ##f_1|_N=f_2|_N##, then ##v(f_1)=v(f_2)## for ##v\in V_p##.

So my trouble is solved.
 

1. What is Wald Theorem 2.2.1 Problem on p.15?

Wald Theorem 2.2.1 Problem on p.15 is a mathematical theorem that relates to the concept of stopping rules and decision making in statistical analysis. It was proposed by mathematician Abraham Wald in his seminal paper "Sequential Tests of Statistical Hypotheses."

2. What does Wald Theorem 2.2.1 state?

Wald Theorem 2.2.1 states that under certain assumptions, if a sequential test is terminated at the first stage, then the resulting test is uniformly most powerful among all one-stage tests with the same significance level.

3. What are the assumptions of Wald Theorem 2.2.1?

The assumptions of Wald Theorem 2.2.1 include a fixed sample size, a binary outcome, and a known probability distribution of the outcome. It also assumes that the test statistic is continuous and the stopping rule is based on it.

4. How does Wald Theorem 2.2.1 relate to decision making in statistics?

Wald Theorem 2.2.1 is used to make optimal decisions in sequential tests, where data is collected and analyzed in stages. It helps determine the most efficient and powerful stopping rule for the test, allowing for more accurate and efficient decision making in statistical analysis.

5. Are there any real-world applications of Wald Theorem 2.2.1?

Yes, Wald Theorem 2.2.1 has various real-world applications in fields such as medicine, engineering, and finance. It is commonly used in clinical trials, quality control processes, and risk management strategies to make optimal decisions based on sequential data analysis.

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