Constrained dynamic futures portfolios with stochastic basis

2021 ◽  
Author(s):  
Xiaodong Chen ◽  
Tim Leung ◽  
Yang Zhou
Keyword(s):  
Stochastic Basis

scholarly journals Weak Orlicz–Hardy martingale spaces

2015 ◽  
Vol 26 (08) ◽  
pp. 1550062 ◽  
Author(s):  
Yong Jiao ◽  
Lian Wu ◽  
Lihua Peng
Keyword(s):  
Atomic Decomposition ◽  
Type Inequality ◽  
Sublinear Operator ◽  
Sufficient Condition ◽  
Concave Functions ◽  
Atomic Decompositions ◽  
Stochastic Basis ◽  
Decomposition Theorems

In this paper, several weak Orlicz–Hardy martingale spaces associated with concave functions are introduced, and some weak atomic decomposition theorems for them are established. With the help of weak atomic decompositions, a sufficient condition for a sublinear operator defined on the weak Orlicz–Hardy martingale spaces to be bounded is given. Further, we investigate the duality of weak Orlicz–Hardy martingale spaces and obtain a new John–Nirenberg type inequality when the stochastic basis is regular. These results can be regarded as weak versions of the Orlicz–Hardy martingale spaces due to Miyamoto, Nakai and Sadasue.

Risk Theory: The Stochastic Basis of Insurance (3rd ed.)

1987 ◽  
Vol 82 (397) ◽  
pp. 362
Author(s):  
Harry H. Panjer ◽  
R. E. Beard ◽  
T. Pentikainen ◽  
E. Pesonen
Keyword(s):  
Risk Theory ◽  
Stochastic Basis

Stochastic-basis matched-field processing

2015 ◽  
Vol 138 (3) ◽  
pp. 1929-1929
Author(s):  
Steven I. Finette ◽  
Peter C. Mignerey ◽  
Roger M. Oba
Keyword(s):  
Matched Field Processing ◽  
Stochastic Basis

ML ESTIMATES FOR CROW/AMSAA RELIABILITY GROWTH MODEL FOR GROUPED AND MIXED TYPES OF SOFTWARE FAILURE DATA

2004 ◽  
Vol 11 (04) ◽  
pp. 329-337
Author(s):  
RANI ◽  
R. B. MISRA
Keyword(s):  
Growth Models ◽  
Quantitative Measure ◽  
Interval Data ◽  
Comprehensive Treatment ◽  
Reliability Growth ◽  
Data Types ◽  
Software Failure ◽  
Failure Data ◽  
Stochastic Basis ◽  
Hybrid Data

A number of software reliability growth models have been proposed into the literature for estimating reliability during software testing. Duane's model,7 originally proposed for hardware reliability is also used in estimating reliability of the software during development testing. Graphical interpretation of Duane's postulate subsequently was given a concrete stochastic basis by Crow,3 and provided a comprehensive treatment of this model in the context of reliability growth and demonstrated its elegant inferential aspects. Parameters of the Crow model have physical interpretation and can yield quantitative measure for reliability growth assessment. This paper proposes a simple and efficient procedure to determine parameters of Crow/AMSAA model using one dimensional bisection method for grouped/interval data, where failures are recorded at various time points. In addition this paper proposes a method to estimate parameters when there exist a mixture of grouped and individual (mixed or hybrid) data types. Proposed method's application is illustrated with numerical examples using both simulated and real software failure data.

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