NPTEL- Probability and Distributions MODULE 3 FUNCTION OF A RANDOM VARIABLE AND ITS DISTRIBUTION LECTURE 16 Topics 3.5 PROBABILITY AND MOMENT INEQUALITIES 3.5.3 Jensen Inequality 3.5.4 푨푴-푮푴-푯푴 inequality 3.6 DESCRIPTIVE MEASURES OF PROBABILITY DISTRIBUTIONS 3.6.1 Measures of Central Tendency 3.6.1.1 Mean 3.6.1.2 Median 3.6.1.3 Mode 3.6.2 Measures of Dispersion 3.6.2.1 Standard Deviation 3.6.2.2 Mean Deviation 3.6.2.3 Quartile Deviation 3.6.2.4 Coefficient of Variation 3.6.3 measures of skewness 3.6.4 measures of kurtosis 3.5.3 Jensen Inequality Theorem 5.2 Let 퐼 ⊆ ℝ be an interval and let 휙: 퐼 → ℝ be a twice differentiable function such that its second order derivative 휙′′ ∙ is continuous on 퐼 and휙′′ 푥 ≥ 0, ∀푥 ∈ ℝ. Let 푋 be a random variable with support 푆푋 ⊆ 퐼 and finite expectation. Then 퐸 휙 푋 ≥ 휙 퐸 푋 . If 휙′′ 푥 > 0, ∀푥 ∈ 퐼, then the inequality above is strict unless 푋 is a degenerate random variable. Dept. of Mathematics and Statistics Indian Institute of Technology, Kanpur 1 NPTEL- Probability and Distributions Proof. Let 휇 = 퐸 푋 . On expanding 휙 푥 into a Taylor series about 휇 we get 푥 − 휇 2 휙 푥 = 휙 휇 + 푥 − 휇 휙′ 휇 + 휙′′ 휉 , ∀푥 ∈ 퐼, 2! for some 휉 between 휇 and 푥. Since 휙′′ 푥 ≥ 0, ∀푥 ∈ 퐼, it follows that ′ 휙 푥 ≥ 휙 휇 + 푥 − 휇 휙 휇 , ∀푥 ∈ 퐼 (5.2) ′ ⟹ 휙 푋 ≥ 휙 휇 + 푋 − 휇 휙 휇 , ∀푋 ∈ 푆푋 ⟹ 퐸 휙 푋 ≥ 휙 휇 + 휙′ 휇 퐸 푋 − 휇 = 휙 휇 = 휙 퐸 푋 . Clearly the inequality in (5.2) is strict unless 퐸 푋 − 휇 2 = 0, i. e., 푃 푋 = 휇 = 1 (using Corollary 3.1 (ii)). ▄ Example 5.3 Let 푋 be a random variable with support 푆푋, an interval in ℝ. Then 2 (i) 퐸 푋2 ≥ 퐸 푋 taking 휙 푥 = 푥2, 푥 ∈ ℝ, in Theorem 5.2 ; (ii) 퐸 ln 푋 ≤ ln 퐸 푋 , provided 푆푋 ⊆ 0, ∞ taking 휙 푥 = − ln 푥, 푥 ∈ ℝ, in Theorem 5.2 ; (iii) 퐸 푒−푋 ≥ 푒−퐸 푋 taking 휙 푥 = 푒−푥 , 푥 ∈ ℝ, in Theorem 5.2 ; 1 1 1 (iv) 퐸 ≥ , if 푆 ⊆ 0, ∞ taking 휙 푥 = , 푥 ∈ ℝ in Theorem 5.2 ; 푋 퐸 푋 푋 푥 provided the involved expectations exist. ▄ Definition 5.2 Let 푋 be a random variable with support 푆푋 ⊆ 0, ∞ . Then, provided they are finite, 퐸 푋 is called the arithmetic mean 퐴푀 of 푋, 푒퐸 ln 푋 is called the geometric mean 1 퐺푀 of 푋, and 1 is called harmonic mean (퐻푀) of 푋. ▄ 퐸 푋 3.5.4 푨푴-푮푴-푯푴 inequality Example 5.4 (i) Let 푋 be a random variable with support 푆푋 ⊆ 0, ∞ . Then Dept. of Mathematics and Statistics Indian Institute of Technology, Kanpur 2 NPTEL- Probability and Distributions 퐸 ln 푋 1 퐸 푋 ≥ 푒 ≥ 1 , 퐸 푋 provided the expectations are finite. (ii) Let 푎1 , ⋯ , 푎푛 be positive real constants and let 푝1 , ⋯ , 푝푛 be another set of positive 푛 real constants such that 푖=1 푝푖 = 1. Then 푛 푛 1 푎 푝 ≥ 푎 푝 ≥ ∙ 푖 푖 푖 푖 푛 푝푖 푖=1 푖=1 푖=1 푎푖 Solution. (i) From Example 5.3 (ii) we have ln 퐸 푋 ≥ 퐸 ln 푋 ⟹ 퐸 푋 ≥ 푒퐸 ln 푋 (5.3) 1 Using (5.3) on ,we get 푋 1 1 퐸 ln 퐸 ≥ 푒 푋 = 푒−퐸 ln 푋 푋 퐸 ln 푋 1 ⟹ 푒 ≥ 1 . (5.4) 퐸 푋 The assertion now follows on combining (5.3) and (5.4). (ii) Let 푋 be a discrete type random variable with support 푆푋 = 푎1, 푎2 ⋯ and 푃 푋 = 푎푖 = 푝푖, 푖 = 1, ⋯ , 푛. Clearly, 푛 푃 푋 = 푥 > 0 ∀푥 ∈ 푆푋 and 푥∈푆푋 푃 푋 = 푥 = 푖=1 푝푖 = 1. On using (i), we get 퐸 ln 푋 1 퐸 푋 ≥ 푒 ≥ 1 퐸 푋 푛 푛 ln 푎 푝 1 ⟹ 푎 푝 ≥ 푒 푖=1 푖 푖 ≥ 푖 푖 푛 푝푖 푖=1 푖=1 푎푖 푛 ln 푛 푎 푝 푖 1 ⟹ 푎 푝 ≥ 푒 푖=1 푖 ≥ 푖 푖 푛 푝푖 푖=1 푖=1 푎푖 Dept. of Mathematics and Statistics Indian Institute of Technology, Kanpur 3 NPTEL- Probability and Distributions 푛 푛 푝 1 ⟹ 푎 푝 ≥ 푎 푖 ≥ . 푖 푖 푖 푛 푝푖 푖=1 푖=1 푖=1 푎푖 3.6 DESCRIPTIVE MEASURES OF PROBABILITY DISTRIBUTIONS Let 푋 be a random variable defined on a probability space 훺, ℱ, 푃 , associated with a random experiment ℰ. Let 퐹푋 and 푓푋 denote, respectively, the distribution function and the p.d.f./p.m.f. of 푋. The probability distribution (i.e., the distribution function/p.d.f./p.m.f.) of 푋 describes the manner in which the random variable 푋 takes values in different Borel sets. It may be desirable to have a set of numerical measures that provide a summary of the prominent features of the probability distribution of 푋. We call these measures as descriptive measures. Four prominently used descriptive measures are: (i) Measures of central tendency (or location), also referred to as averages; (ii) measures of dispersion; (iii) measures of skewness, and (iv) measures of kurtosis. 3.6.1 Measures of Central Tendency A measure of central tendency or location (also called an average) gives us the idea about the central value of the probability distribution around which values of the random variable are clustered. Three commonly used measures of central tendency are mean, median and mode. 3.6.1.1 Mean. Recall (Definition 3.2 (i)) that the mean (of probability distribution) of a random variable ′ 푋 is given by 휇1 = 퐸 푋 . We have seen that the mean of a probability distribution gives us idea about the average observed value of 푋 in the long run (i.e., the average of observed values of 푋when the random experiment is repeated a large number of times). Mean seems to be the best suited average if the distribution is symmetric about a point 푑 휇 (i.e., 푋 − 휇 = 휇 − 푋 , in which case 휇 = 퐸 푋 provided it is finite), values in the neighborhood of 휇 occur with high probabilities, and as we move away from 휇 in either direction 푓푋(⋅) decreases. Because of its simplicity mean is the most commonly used average (especially for symmetric or nearly symmetric distributions). Some of the demerits of this measure are that in some situations this may not be defined (Examples 3.2 and 3.4) and that it is very sensitive to presence of a few extreme values of 푋 which are different from other values of 푋 (even though they may occur with small positive Dept. of Mathematics and Statistics Indian Institute of Technology, Kanpur 4 NPTEL- Probability and Distributions probabilities). So this measure should be used with caution if probability distribution assigns positive probabilities to a few Borel sets having some extreme values. 3.6.1.2 Median. 1 1 A real number 푚 satisfying in 퐹 푚 − ≤ ≤ 퐹 푚 , i. e. , 푃 푋 < 푚 ≤ ≤ 푋 2 푋 2 푃 푋 ≤ 푚 , is called the median (of the probability distribution) of 푋. Clearly if 푚 is the median of a probability distribution then, in the long run (i.e., when the random experiment ℰ is repeated a large number of times), the values of 푋 on either side of 푚 in 푆푋 are observed with the same frequency. Thus the median of a probability distribution, in some sense, divides 푆푋 into two equal parts each having the same probability of occurrence. It is evident that if 푋is of continuous type then the median m is given by 퐹푋 푚 = 1/2. For some distributions (especially for distributions of discrete type 1 random variable) it may happen that 퐹 푎 − < and 푥 ∈ ℝ: 퐹 푥 = 1/2 = 푎 , 푏 , 푋 2 푋 for some −∞ < 푎 < 푏 < ∞, so that the median is not unique. In that case 푃 푋 = 푥 = 0, ∀x ∈ a, b and thus we take the median to be 푚 = 푎 = inf 푥 ∈ ℝ: 퐹푋 푥 ≥ 1/2 . For random variables having a symmetric probability distribution it is easy to verify that the mean and the median coincide (see Problem 33). Unlike the mean, the median of a probability distribution is always defined. Moreover the median is not affected by a few extreme values as it takes into account only the probabilities with which different values occur and not their numerical values. As a measure of central tendency the median is preferred over the mean if the distribution is asymmetric and a few extreme observations are assigned positive probabilities. However the fact that the median does not at all take into account the numerical values of 푋 is one of its demerits. Another disadvantage with median is that for many probability distributions it is not easy to evaluate (especially for distributions whose distribution functions 퐹푋(⋅) do not have a closed form). 3.6.1.3 Mode. Roughly speaking the mode 푚0 of a probability distribution is the value that occurs with highest probability and is defined by 푓푋 푚0 = sup 푓푋 푥 : 푥 ∈ 푆푋 . Clearly if 푚0 is the mode of a probability distribution of 푋 then, in the long run, either 푚0 or a value in the neighborhood of 푚0 is observed with maximum frequency. Mode is easy to understand and easy to calculate. Normally, it can be found by just inspection.