On Some Properties of the Multivariate Aging Classes
01 Oct 1991-Probability in the Engineering and Informational Sciences (Cambridge University Press)-Vol. 5, Iss: 04, pp 523-534
About: This article is published in Probability in the Engineering and Informational Sciences.The article was published on 1991-10-01. It has received 2 citations till now. The article focuses on the topics: Multivariate statistics.
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TL;DR: In this article, the authors study some properties for multivariate weighted distributions related to reliability measures, ordering, characterization and dependence properties, by compiling and extending previous results given by different authors.
Abstract: We study some properties for multivariate weighted distributions related to reliability measures, ordering, characterization and dependence properties, by compiling and extending previous results given by different authors. We pay special attention to the multivariate size biased and equilibrium distributions, and propose a new definition for the multivariate equilibrium distribution.
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01 Jan 1963
TL;DR: These notes cover the basic definitions of discrete probability theory, and then present some results including Bayes' rule, inclusion-exclusion formula, Chebyshev's inequality, and the weak law of large numbers.
Abstract: These notes cover the basic definitions of discrete probability theory, and then present some results including Bayes' rule, inclusion-exclusion formula, Chebyshev's inequality, and the weak law of large numbers. 1 Sample spaces and events To treat probability rigorously, we define a sample space S whose elements are the possible outcomes of some process or experiment. For example, the sample space might be the outcomes of the roll of a die, or flips of a coin. To each element x of the sample space, we assign a probability, which will be a non-negative number between 0 and 1, which we will denote by p(x). We require that x∈S p(x) = 1, so the total probability of the elements of our sample space is 1. What this means intuitively is that when we perform our process, exactly one of the things in our sample space will happen. Example. The sample space could be S = {a, b, c}, and the probabilities could be p(a) = 1/2, p(b) = 1/3, p(c) = 1/6. If all elements of our sample space have equal probabilities, we call this the uniform probability distribution on our sample space. For example, if our sample space was the outcomes of a die roll, the sample space could be denoted S = {x 1 , x 2 ,. .. , x 6 }, where the event x i correspond to rolling i. The uniform distribution, in which every outcome x i has probability 1/6 describes the situation for a fair die. Similarly, if we consider tossing a fair coin, the outcomes would be H (heads) and T (tails), each with probability 1/2. In this situation we have the uniform probability distribution on the sample space S = {H, T }. We define an event A to be a subset of the sample space. For example, in the roll of a die, if the event A was rolling an even number, then A = {x 2 , x 4 , x 6 }. The probability of an event A, denoted by P(A), is the sum of the probabilities of the corresponding elements in the sample space. For rolling an even number, we have P(A) = p(x 2) + p(x 4) + p(x 6) = 1 2 Given an event A of our sample space, there is a complementary event which consists of all points in our sample space that are not …
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TL;DR: In this article, a vector definition of multivariate hazard rate and associated definitions of increasing and decreasing hazard rate distributions are presented, and the results of these definitions are worked out in a number of special cases.
238 citations