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Some of the next articles are maybe not open access.
2000
The Central Limit Theorem is one of the most impressive achievements of probability theory. Prom a simple description requiring minimal hypotheses, we are able to deduce precise results. The Central Limit Theorem thus serves as the basis for much of Statistical Theory. The idea is simple: let X1,...,X j ,... be a sequence of i.i.d.
Jean Jacod, Philip Protter
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The Central Limit Theorem is one of the most impressive achievements of probability theory. Prom a simple description requiring minimal hypotheses, we are able to deduce precise results. The Central Limit Theorem thus serves as the basis for much of Statistical Theory. The idea is simple: let X1,...,X j ,... be a sequence of i.i.d.
Jean Jacod, Philip Protter
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1989
Actually, we had a central limit theorem (CLT) in lesson 14: the sequence X1X2X3… consists of IID Bernoulli RVs with parameter 0; the partial sum \({S_{n}} = \sum\nolimits_{{k = 1}}^{n} {{X_{k}}}\) is standardized to $${V_{n}} = ({S_{n}} - n\theta )/\surd (n\theta (1 - \theta );$$ the limiting distribution of Vnis normal.
Hung T. Nguyen, Gerald S. Rogers
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Actually, we had a central limit theorem (CLT) in lesson 14: the sequence X1X2X3… consists of IID Bernoulli RVs with parameter 0; the partial sum \({S_{n}} = \sum\nolimits_{{k = 1}}^{n} {{X_{k}}}\) is standardized to $${V_{n}} = ({S_{n}} - n\theta )/\surd (n\theta (1 - \theta );$$ the limiting distribution of Vnis normal.
Hung T. Nguyen, Gerald S. Rogers
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2018
Let P be a Markov kernel on \(\mathsf {X}\times \mathscr {X}\) that admits an invariant probability measure \(\pi \) and let Open image in new window be the canonical Markov chain.
Randal Douc +3 more
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Let P be a Markov kernel on \(\mathsf {X}\times \mathscr {X}\) that admits an invariant probability measure \(\pi \) and let Open image in new window be the canonical Markov chain.
Randal Douc +3 more
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2019
In this chapter, we will discuss central limit theorems for some of the functionals introduced in Chapter 2. Further we develop general techniques that will later in Chapters 7–9 be applied to find central limit theorems also for other more specific statistics which are based on functionals from Chapter 2.
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In this chapter, we will discuss central limit theorems for some of the functionals introduced in Chapter 2. Further we develop general techniques that will later in Chapters 7–9 be applied to find central limit theorems also for other more specific statistics which are based on functionals from Chapter 2.
openaire +1 more source