Lecture 3
... Weakly Dependent Time Series A very different concept from stationarity. Weakly Dependent Process A stationary time series process {xt : t = 1, 2, . . .} is said to be weakly dependent if xt and xt+h are “almost independent” as h increases. Very vague definition, as there are many cases of weak dep ...
... Weakly Dependent Time Series A very different concept from stationarity. Weakly Dependent Process A stationary time series process {xt : t = 1, 2, . . .} is said to be weakly dependent if xt and xt+h are “almost independent” as h increases. Very vague definition, as there are many cases of weak dep ...
Homework set 6 Characteristic functions, CLT Further Topics in
... Below you will need the Central Limit Theorem: Theorem 2 Let Xi be iid. random variables with finite mean m and variance σ 2 . Then for every a ∈ R, P ...
... Below you will need the Central Limit Theorem: Theorem 2 Let Xi be iid. random variables with finite mean m and variance σ 2 . Then for every a ∈ R, P ...
homework_hints
... “TRUE” in the “Cumulative” box). Remember, your z-score will be negative, and you’re looking for the area under the curve (the probability) of getting a sales average LESS than $400,000….so you’re looking for the area under the curve to the left of that value. For 7.32 b, review the discussion of th ...
... “TRUE” in the “Cumulative” box). Remember, your z-score will be negative, and you’re looking for the area under the curve (the probability) of getting a sales average LESS than $400,000….so you’re looking for the area under the curve to the left of that value. For 7.32 b, review the discussion of th ...
Heart Rate Variability: Measures and Models
... The Allan factor is the ratio of the eventnumber Allan variance to twice the mean: ...
... The Allan factor is the ratio of the eventnumber Allan variance to twice the mean: ...
F - BME
... Probability Distribution Function: PDF(x) = P{ f < x } Probability Density Function: pdf(x) = d PDF(x) / dx Expected value: E[ f ] = f(x)·pdf(x) dx ...
... Probability Distribution Function: PDF(x) = P{ f < x } Probability Density Function: pdf(x) = d PDF(x) / dx Expected value: E[ f ] = f(x)·pdf(x) dx ...
