Characterizations using record moments in a random record model and applications

We consider a random record model from a continuous parent X with cumulative distribution function F, where the number of available observations is geometrically distributed. We show that, if E(|X|) is finite, then so is E(|Rn|) whenever Rn, the nth upper record value, exists. We prove that appropriately chosen subsequences of E(Rn) characterize F and subsequences of E(Rn − Rn−1) identify F up to a location shift. We discuss some applications to the identification of wage-offer distributions in job search models.

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Characterizations using record moments in a random record model and applications

Journal of Applied Probability ◽

10.1017/s0021900200019781 ◽

2003 ◽

Vol 40 (03) ◽

pp. 826-833 ◽

Cited By ~ 2

Author(s):

H. N. Nagaraja ◽

Gadi Barlevy

Keyword(s):

Distribution Function ◽

Job Search ◽

Cumulative Distribution Function ◽

Cumulative Distribution ◽

Search Models ◽

Location Shift ◽

Record Value ◽

Upper Record

We consider a random record model from a continuous parent X with cumulative distribution function F, where the number of available observations is geometrically distributed. We show that, if E(|X|) is finite, then so is E(|R n |) whenever R n , the nth upper record value, exists. We prove that appropriately chosen subsequences of E(R n ) characterize F and subsequences of E(R n − R n−1) identify F up to a location shift. We discuss some applications to the identification of wage-offer distributions in job search models.

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Characterizations in a random record model with a nonidentically distributed initial record

Journal of Applied Probability ◽

10.1017/s0021900200002473 ◽

2006 ◽

Vol 43 (04) ◽

pp. 1119-1136 ◽

Cited By ~ 3

Author(s):

Gadi Barlevy ◽

H. N. Nagaraja

Keyword(s):

Distribution Function ◽

Job Search ◽

Cumulative Distribution Function ◽

Cumulative Distribution ◽

Search Models ◽

Common Location ◽

Location Shift ◽

Record Value

We consider a sequence, of random length M, of independent, continuous observations X i , 1 ≤ i ≤ M, where M is geometric, X 1 has cumulative distribution function (CDF) G, and X i , i ≥ 2, have CDF F. Let N be the number of upper records and let R n , n ≥ 1, be the nth record value. We show that N is independent of F if and only if G(x) = G 0(F(x)) for some CDF G 0, and that if E(|X 2|) is finite then so is E(|R n |), n ≥ 2, whenever N ≥ n or N = n. We prove that the distribution of N, along with appropriately chosen subsequences of E(R n ), characterize F and G and, along with subsequences of E(R n - R n-1), characterize F and G up to a common location shift. We discuss some applications to the identification of the wage offer distribution in job search models.

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Characterizations in a random record model with a nonidentically distributed initial record

Journal of Applied Probability ◽

10.1239/jap/1165505212 ◽

2006 ◽

Vol 43 (4) ◽

pp. 1119-1136 ◽

Cited By ~ 8

Author(s):

Gadi Barlevy ◽

H. N. Nagaraja

Keyword(s):

Distribution Function ◽

Job Search ◽

Cumulative Distribution Function ◽

Cumulative Distribution ◽

Search Models ◽

Common Location ◽

Location Shift ◽

Record Value

We consider a sequence, of random length M, of independent, continuous observations Xi, 1 ≤ i ≤ M, where M is geometric, X1 has cumulative distribution function (CDF) G, and Xi, i ≥ 2, have CDF F. Let N be the number of upper records and let Rn, n ≥ 1, be the nth record value. We show that N is independent of F if and only if G(x) = G0(F(x)) for some CDF G0, and that if E(|X2|) is finite then so is E(|Rn|), n ≥ 2, whenever N ≥ n or N = n. We prove that the distribution of N, along with appropriately chosen subsequences of E(Rn), characterize F and G and, along with subsequences of E(Rn - Rn-1), characterize F and G up to a common location shift. We discuss some applications to the identification of the wage offer distribution in job search models.

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Gradient based empirical cumulative distribution function approximation for robust aerodynamic design

Aerospace Science and Technology ◽

10.1016/j.ast.2021.106630 ◽

2021 ◽

pp. 106630

Author(s):

Elisa Morales ◽

Andrea Bornaccioni ◽

Domenico Quagliarella ◽

Renato Tognaccini

Keyword(s):

Distribution Function ◽

Function Approximation ◽

Cumulative Distribution Function ◽

Cumulative Distribution ◽

Aerodynamic Design ◽

Empirical Cumulative Distribution Function ◽

Gradient Based

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Determinants of the Strategic Mortgage Default Cumulative Distribution Function

Journal of Real Estate Literature ◽

10.1080/10835547.2016.12090420 ◽

2016 ◽

Vol 24 (1) ◽

pp. 183-199

Author(s):

Michael J. Seiler

Keyword(s):

Distribution Function ◽

Cumulative Distribution Function ◽

Cumulative Distribution ◽

Mortgage Default

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DISSONANCE — A MEASURE OF VARIABILITY FOR ORDINAL RANDOM VARIABLES

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488501000594 ◽

2001 ◽

Vol 09 (01) ◽

pp. 39-53 ◽

Cited By ~ 2

Author(s):

RONALD R. YAGER

Keyword(s):

Distribution Function ◽

Cumulative Distribution Function ◽

Probability Distributions ◽

Random Variables ◽

Cumulative Distribution ◽

General Properties

We look at the issue of obtaining a variance like measure associated with probability distributions over ordinal sets. We call these dissonance measures. We specify some general properties desired in these dissonance measures. The centrality of the cumulative distribution function in formulating the concept of dissonance is pointed out. We introduce some specific examples of measures of dissonance.

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