Certain Estimation Problems in Dependability using Percentiles
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In statistical estimation problems, we use random variables X1, X2,..., Xn that are independent and identically distributed as random variable X whose probability distribution is known but involves certain unknown parameters ¿ = (¿1, ¿2,..., ¿k) , (k < n), which are labelling or indexing parameters. The parameters ¿1, ¿2,..., ¿k are not random variables. We will consider the following problem. The random variables X1, X2,..., Xn have a common distribution of continuous type defined by the probability density function f (x1, x2, . . . , xn; ¿1, ¿2,..., ¿k). We observe a point x = (x1,...
In statistical estimation problems, we use random variables X1, X2,..., Xn that are independent and identically distributed as random variable X whose probability distribution is known but involves certain unknown parameters ¿ = (¿1, ¿2,..., ¿k) , (k < n), which are labelling or indexing parameters. The parameters ¿1, ¿2,..., ¿k are not random variables. We will consider the following problem. The random variables X1, X2,..., Xn have a common distribution of continuous type defined by the probability density function f (x1, x2, . . . , xn; ¿1, ¿2,..., ¿k). We observe a point x = (x1, x2, . . . , xn) ¿ X ¿ Rn of the variables where X is the sample space. It is required to use these observed values x1, x2, . . . , xn to find estimates of unknown parameters ¿1, ¿2,..., ¿k. We consider random sample defined as Definition 1.1.1 The random variables X1, X2,..., Xn are called a random sample of size n from the population f (x; ¿),¿ ¿ ¿ ¿ Rk, if X1, X2,..., Xn are mutually independent random variables and the marginal pdf or pmf of each Xi is the same function f (x; ¿). Alternatively, X1, X2,..., Xn are called independent and identically distributed (iid) random variables with pdf or pmf f (x; ¿).
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