In this paper, let be a sequence of k-record values from a population with common distribution function F. We will characterize the continuous (or discrete) distribution function F by the conditional expectation functions. We also study the necessary and sufficient conditions such that the conditional expectations of k-record values hold for some distribution function F. A corresponding characterization based on weak k-record values and some related characterizations are also investigated.

This study revolves around operators defined by conditional expectations and operators generated by random measures. Studies of operators in function spaces defined by conditional expectations first appeared in the mid 1950's by S-T.C. Moy [22] and S. Sidak [26]. N. Kalton studied them in the setting of Lp-spaces 0 < p < 1 in [15, 131 and in L1-spaces, [14], while W. Arveson [5] studied them in L2-spaces. Their averaging properties were studied by P.G. Dodds and C.B. Huijsmans and B. de Pagter in [7] and C.B. Huijsmans and B. de Pagter in [lo]. A. Lambert [17] studied their relationship with multiplication operators in C*-modules. It was shown by J.J. Grobler and B. de Pagter [8] that partial integral operators that were studied A.S. Kalitvin et a1 in [2, 4, 3, 11, 121 and the special cases of kernel operators that were, inter alia, studied by A.R. Schep in [25] were special cases of conditional expectation operators. On the other hand, operators generated by random measures or pseudo-integral operators were studied by A. Sourour [28, 271 and L.W. Weis [29,30], building on the studies of W. Arveson [5] and N. Kalton [14, 151, in the late 1970's and early 1980's. In this thesis we extend the work of J.J. Grobler and B. de Pagter [8] on Multiplication Conditional Expectation-representable (MCE-representable) operators. We also generalize the result of A. Sourour [27] and show that order continuous linear maps between ideals of almost everywhere finite measurable functions on u-finite measure spaces are MCE-representable. This fact enables us to easily deduce that sums and compositions of MCE-representable operators are again MCE-representable operators. We also show that operators generated by random measures are MCE-representable. The first chapter gathers the definitions and introduces notions and concepts that are used throughout. In particular, we introduce Riesz spaces and operators therein, Riesz and Boolean homomorphisms, conditional expectation operators, kernel and absolute T-kernel operators. In Chapter 2 we look at MCE-operators where we give a definition different from that given by J.J. Grobler and B. de Pagter in [8], but which we show to be equivalent. Chapter 3 involves random measures and operators generated by random measures. We solve the problem (positively) that was posed by A. Sourour in [28] about the relationship of the lattice properties of operators generated by random measures and the lattice properties of their generating random measures. We show that the total variation of a random signed measure representing an order bounded operator T, it being the difference of two random measures, is again a random measure and represents ITI. We also show that the set of all operators generated by a random measure is a band in the Riesz space of all order bounded operators. In Chapter 4 we investigate the relationship between operators generated by random measures and MCE-representable operators. It was shown by A. Sourour in [28, 271 that every order bounded order continuous linear operator acting between ideals of almost everywhere measurable functions is generated by a random measure, provided that the measure spaces involved are standard measure spaces. We prove an analogue of this theorem for the general case where the underlying measure spaces are a-finite. We also, in this general setting, prove that every order continuous linear operator is MCE-representable. This rather surprising result enables us to easily show that sums, products and compositions of MCE-representable operator are again MCE-representable. Key words: Riesz spaces, conditional expectations, multiplication conditional expectation-representable operators, random measures. / Thesis (Ph.D. (Mathematics))--North-West University, Potchefstroom Campus, 2004.

This study revolves around operators defined by conditional expectations and operators generated by random measures. Studies of operators in function spaces defined by conditional expectations first appeared in the mid 1950's by S-T.C. Moy [22] and S. Sidak [26]. N. Kalton studied them in the setting of Lp-spaces 0 < p < 1 in [15, 131 and in L1-spaces, [14], while W. Arveson [5] studied them in L2-spaces. Their averaging properties were studied by P.G. Dodds and C.B. Huijsmans and B. de Pagter in [7] and C.B. Huijsmans and B. de Pagter in [lo]. A. Lambert [17] studied their relationship with multiplication operators in C*-modules. It was shown by J.J. Grobler and B. de Pagter [8] that partial integral operators that were studied A.S. Kalitvin et a1 in [2, 4, 3, 11, 121 and the special cases of kernel operators that were, inter alia, studied by A.R. Schep in [25] were special cases of conditional expectation operators. On the other hand, operators generated by random measures or pseudo-integral operators were studied by A. Sourour [28, 271 and L.W. Weis [29,30], building on the studies of W. Arveson [5] and N. Kalton [14, 151, in the late 1970's and early 1980's. In this thesis we extend the work of J.J. Grobler and B. de Pagter [8] on Multiplication Conditional Expectation-representable (MCE-representable) operators. We also generalize the result of A. Sourour [27] and show that order continuous linear maps between ideals of almost everywhere finite measurable functions on u-finite measure spaces are MCE-representable. This fact enables us to easily deduce that sums and compositions of MCE-representable operators are again MCE-representable operators. We also show that operators generated by random measures are MCE-representable. The first chapter gathers the definitions and introduces notions and concepts that are used throughout. In particular, we introduce Riesz spaces and operators therein, Riesz and Boolean homomorphisms, conditional expectation operators, kernel and absolute T-kernel operators. In Chapter 2 we look at MCE-operators where we give a definition different from that given by J.J. Grobler and B. de Pagter in [8], but which we show to be equivalent. Chapter 3 involves random measures and operators generated by random measures. We solve the problem (positively) that was posed by A. Sourour in [28] about the relationship of the lattice properties of operators generated by random measures and the lattice properties of their generating random measures. We show that the total variation of a random signed measure representing an order bounded operator T, it being the difference of two random measures, is again a random measure and represents ITI. We also show that the set of all operators generated by a random measure is a band in the Riesz space of all order bounded operators. In Chapter 4 we investigate the relationship between operators generated by random measures and MCE-representable operators. It was shown by A. Sourour in [28, 271 that every order bounded order continuous linear operator acting between ideals of almost everywhere measurable functions is generated by a random measure, provided that the measure spaces involved are standard measure spaces. We prove an analogue of this theorem for the general case where the underlying measure spaces are a-finite. We also, in this general setting, prove that every order continuous linear operator is MCE-representable. This rather surprising result enables us to easily show that sums, products and compositions of MCE-representable operator are again MCE-representable. Key words: Riesz spaces, conditional expectations, multiplication conditional expectation-representable operators, random measures. / Thesis (Ph.D. (Mathematics))--North-West University, Potchefstroom Campus, 2004.

Seja {Xn} uma sequência de variáveis aleatórias i.i.d. assumindo valores num alfabeto enumerável. Dada uma coleção de palavras finita, observamos esta sequência até o momento &tau; em que uma dessas palavras apareça em X1, X2, .....Neste trabalho utilizamos a abordagem de martingais, introduzida por Li (1980) e Gerber e Li (1981), para estudar o tempo de espera até que uma das palavras ocorra pela primeira vez, o tempo médio de &tau; e a probabilidade de uma palavra ser a primeira a aparecer. / Let {Xn} be a sequence of i.i.d. random variables talking values in an enumerable alphgabet. Given a finite collection of words, we observe this sequence till the moment &tau; at which one of these words appears as a run. In this work we apply the martingale approach introduced by Li (1980) and Gerber and Li (1981) in order to study the waiting time until one of the words occurs for the first time, the mean of &tau; and the probability of a word to be first on to appear.

The study of properties of probability distributions has always been a persistent theme of statistics and of applied probability. This thesis deals with an investigation of distribution functions under the following two topics: (i) characterization of distributions based on record values and order statistics, (ii) properties of the skew-t distribution. Within the extensive characterization literature there are several results involving properties of record values and order statistics. Although there have been many well known results already developed, it is still of great interest to find new characterization of distributions based on record values and order statistics. In the first part, we provide the conditional distribution of any record value given the maximum order statistics and study characterizations of distributions based on record values and the maximum order statistics. We also give some characterizations of the mean value function within the class of order statistics point processes, by using certain relations between the conditional moments of the jump times or current lives. These results can be applied to characterize the uniform distribution using the sequence of order statistics, and the exponential distribution using the sequence of record values, respectively. Azzalini (1985, 1986) introduced the skew-normal distribution which includes the normal distribution and has some properties like the normal and yet is skew. This class of distributions is useful in studying robustness and for modeling skewness. Since then, skew-symmetric distributions have been proposed by many authors. In the second part, the so-called generalized skew-t distribution is defined and studied. Examples of distributions in this class, generated by the ratio of two independent skew-symmetric distributions, are given. We also investigate properties of the skew-symmetric distribution.

Submitted by Ronildo Prado (ronisp@ufscar.br) on 2017-08-16T18:49:11Z No. of bitstreams: 1 DissVM.pdf: 10400529 bytes, checksum: 6f3a8dfea497dd3a1543a2b5847ad36e (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-16T18:49:21Z (GMT) No. of bitstreams: 1 DissVM.pdf: 10400529 bytes, checksum: 6f3a8dfea497dd3a1543a2b5847ad36e (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-16T18:49:27Z (GMT) No. of bitstreams: 1 DissVM.pdf: 10400529 bytes, checksum: 6f3a8dfea497dd3a1543a2b5847ad36e (MD5) / Made available in DSpace on 2017-08-16T18:49:35Z (GMT). No. of bitstreams: 1 DissVM.pdf: 10400529 bytes, checksum: 6f3a8dfea497dd3a1543a2b5847ad36e (MD5) Previous issue date: 2017-04-07 / Não recebi financiamento / Let {Xn} be a sequence of i.i.d. random variables taking values in an enumerable alphabet. Given a finite collection of words, we observe this sequence till the moment T at which one of these words appears as a run. In this work we apply the martingale approach introduced by Li (1980) and Gerber e Li (1981) in order to study the waiting time until one of the words occurs for the first time, the mean of T and the probability of a word to be the first one to appear. / Seja {Xn} uma sequência de variáveis aleatórias i.i.d. assumindo valores num alfabeto enumerável. Dada uma coleção de palavras finita, observamos esta sequência até o momento T em que uma dessas palavras apareça emX1,X2, .... Neste trabalho utilizamos a abordagem de martingais, introduzida por Li (1980) e Gerber e Li ( 981), para estudar o tempo de espera até que uma das palavras ocorra pela primeira vez, o tempo médio de T e a probabilidade de uma palavra ser a primeira a aparecer.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-07-11T20:52:36Z No. of bitstreams: 1 ANTONIO LUIZ SOARES SANTOS - DISSERTAÇÃO PPGMAT 2007..pdf: 419776 bytes, checksum: 648bbdd93c2d2fdd67f2dd99256a1ff7 (MD5) / Made available in DSpace on 2018-07-11T20:52:36Z (GMT). No. of bitstreams: 1 ANTONIO LUIZ SOARES SANTOS - DISSERTAÇÃO PPGMAT 2007..pdf: 419776 bytes, checksum: 648bbdd93c2d2fdd67f2dd99256a1ff7 (MD5) Previous issue date: 2007-02 / Neste trabalho estudamos o processo de risco a tempo discreto, considerado modelo clássico na teoria do risco, com variantes propostas por Jun Cai e David Dickson (2004). Serão incluídas taxas de juros, as quais seguem uma Cadeia de Markov, e seus efeitos, em relação à probabilidade de ruína serão analisados. O conhecido limitante superior proposto por Lundberg para essa probabilidade fica reduzido em virtude dessa nova abordagem e a desigualdade clássica é generalizada. / In this work we study discrete time risk process considered classical model, with variants proposed by Jun Cai and David Dickson (2004). Rates of interest, which follows a Markov chain will be introduced and their effect on the ruin probabilities will be analysed. Generalized Lundberg inequalities will be obtained and shown how the classical bounds for the ruin probability can be derived.