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A robust estimation based on maximum likelihood arguments.

A random process whose future probabilities are determined by its most recent values. A stochastic process x(t) is called Markov if for every n and t_1<t_2...<t_n, we have ...

A sequence X_1, X_2, ... of random variates is called Markov (or Markoff) if, for any n, F(X_n|X_(n-1),X_(n-2),...,X_1)=F(X_n|X_(n-1)), i.e., if the conditional distribution ...

The statistical index P_(ME)=(sump_n(q_0+q_n))/(sum(v_0+v_n)), where p_n is the price per unit in period n, q_n is the quantity produced in period n, and v_n=p_nq_n is the ...

A matrix each of whose elements is a variate. These variates need not be independent, and if they are not, a correlation is said to exist between them.

A maximum likelihood estimator is a value of the parameter a such that the likelihood function is a maximum (Harris and Stocket 1998, p. 824).

For an infinite population with mean mu, variance sigma^2, skewness gamma_1, and kurtosis excess gamma_2, the corresponding quantities for the distribution of means are ...

The approximating polynomial which has the smallest maximum deviation from the true function. It is closely approximated by the Chebyshev polynomials of the first kind.

The statistical index P_M=(sump_nq_a)/(sump_0q_a), where p_n is the price per unit in period n and q_n is the quantity produced in period n.

Possessing more than one mode. A set of values having a single unique mode is said to be unimodal, one with two modes is called bimodal, and one with three modes is called ...