Add to ORKGStochastic models approximate data and are not true representations of the same. Statistical procedures make use of approximate stochastic models to facilitate the analysis of data
Abstract. We develop a new direct approach to approximating suprema of general empirical processes b...
In this article, we obtain strong Korovkin-type approximation theorems for stochastic processes by u...
There are two major types of data sources that can be used when a phenomenon or a variable is invest...
SIGLEAvailable from TIB Hannover: RR 8460(2003,7) / FIZ - Fachinformationszzentrum Karlsruhe / TIB -...
Statistics has no concept of approximation. Look up the word ‘approximation ’ in the index of any bo...
Abstract. A theoretical model is worked out for statistical data processing. The model can be used t...
IRI Technical ReportIn this work we explain how the stochastic approximation of the average of a ran...
Often scientific information on various data generating processes are presented in the from of numer...
There are essentially two statistical paradigms, the Bayesian and frequentist. Despite their obvious...
The first purpose of this paper is to enunciate our general viewpoints how to formulate our stochast...
Stochastic approximation algorithms are iterative procedures which are used to approximate a target ...
10.1109/IEEM.2010.5674279IEEM2010 - IEEE International Conference on Industrial Engineering and Engi...
center, shape and spread and described how the validity of many statistical procedures relies on an ...
Data produced by the model described in the paper and used for statistical analyses
In stochastic programming, statistics, or econometrics, the aim is in general the optimization of a ...
Abstract. We develop a new direct approach to approximating suprema of general empirical processes b...
In this article, we obtain strong Korovkin-type approximation theorems for stochastic processes by u...
There are two major types of data sources that can be used when a phenomenon or a variable is invest...
SIGLEAvailable from TIB Hannover: RR 8460(2003,7) / FIZ - Fachinformationszzentrum Karlsruhe / TIB -...
Statistics has no concept of approximation. Look up the word ‘approximation ’ in the index of any bo...
Abstract. A theoretical model is worked out for statistical data processing. The model can be used t...
IRI Technical ReportIn this work we explain how the stochastic approximation of the average of a ran...
Often scientific information on various data generating processes are presented in the from of numer...
There are essentially two statistical paradigms, the Bayesian and frequentist. Despite their obvious...
The first purpose of this paper is to enunciate our general viewpoints how to formulate our stochast...
Stochastic approximation algorithms are iterative procedures which are used to approximate a target ...
10.1109/IEEM.2010.5674279IEEM2010 - IEEE International Conference on Industrial Engineering and Engi...
center, shape and spread and described how the validity of many statistical procedures relies on an ...
Data produced by the model described in the paper and used for statistical analyses
In stochastic programming, statistics, or econometrics, the aim is in general the optimization of a ...
Abstract. We develop a new direct approach to approximating suprema of general empirical processes b...
In this article, we obtain strong Korovkin-type approximation theorems for stochastic processes by u...
There are two major types of data sources that can be used when a phenomenon or a variable is invest...