<p>This thesis consists of three papers on the following topics in functional analysis and probability theory: Riesz bases and frames, weakly stationary stochastic processes and analysis of set-valued stochastic processes. In the first paper we investigate Uniformly Bounded Linearly Stationary stochastic processes from the point of view of the theory of Riesz bases. By regarding these stochastic processes as generalized Riesz bases we are able to gain some new insight into there structure. Special attention is paid to regular UBLS processes as well as perturbations of weakly stationary processes. An infinite sequence of subspaces of a Hilbert space is called regular if it is decreasing and zero is the only element in its intersection. In the second paper we ask for conditions under which the regularity of a sequence of subspaces is preserved when the sequence undergoes a deformation by a linear and bounded operator. Linear, bounded and surjective operators are closely linked with frames and we also investigate when a frame is a regular sequence of vectors. A multiprocess is a stochastic process whose values are compact sets. As generalizations of the class of subharmonic processes and the class of subholomorphic processesas introduced by Thomas Ransford, in the third paper of this thesis we introduce the general notions of a gauge of processes and a multigauge of multiprocesses. Compositions of multiprocesses with multifunctions are discussed and the boundary crossing property, related to the intermediate-value property, is investigated for general multiprocesses. Time changes of multiprocesses are investigated in the environment of multigauges and we give a multiprocess version of the Dambis-Dubins-Schwarz Theorem.</p>

This thesis consists of three papers on the following topics in functional analysis and probability theory: Riesz bases and frames, weakly stationary stochastic processes and analysis of set-valued stochastic processes. In the first paper we investigate Uniformly Bounded Linearly Stationary stochastic processes from the point of view of the theory of Riesz bases. By regarding these stochastic processes as generalized Riesz bases we are able to gain some new insight into there structure. Special attention is paid to regular UBLS processes as well as perturbations of weakly stationary processes. An infinite sequence of subspaces of a Hilbert space is called regular if it is decreasing and zero is the only element in its intersection. In the second paper we ask for conditions under which the regularity of a sequence of subspaces is preserved when the sequence undergoes a deformation by a linear and bounded operator. Linear, bounded and surjective operators are closely linked with frames and we also investigate when a frame is a regular sequence of vectors. A multiprocess is a stochastic process whose values are compact sets. As generalizations of the class of subharmonic processes and the class of subholomorphic processesas introduced by Thomas Ransford, in the third paper of this thesis we introduce the general notions of a gauge of processes and a multigauge of multiprocesses. Compositions of multiprocesses with multifunctions are discussed and the boundary crossing property, related to the intermediate-value property, is investigated for general multiprocesses. Time changes of multiprocesses are investigated in the environment of multigauges and we give a multiprocess version of the Dambis-Dubins-Schwarz Theorem.

This thesis is concerned with simulation output analysis. In particular, we are inter- ested in estimating the variance parameter of a steady-state output process. The estimation of the variance parameter has immediate applications in problems involving (i) the precision of the sample mean as a point estimator for the steady-state mean and #956;X, and (ii) confidence intervals for and #956;X. The thesis focuses on new variance estimators arising from Schrubens method of standardized time series (STS). The main idea behind STS is to let such series converge to Brownian bridge processes; then their properties are used to derive estimators for the variance parameter. Following an idea from Shorack and Wellner, we study different levels of folded Brownian bridges. A folded Brownian bridge is obtained from the standard Brownian bridge process by folding it down the middle and then stretching it so that it spans the interval [0,1]. We formulate the folded STS, and deduce a simplified expression for it. Similarly, we define the weighted area under the folded Brownian bridge, and we obtain its asymptotic properties and distribution. We study the square of the weighted area under the folded STS (known as the folded area estimator ) and the weighted area under the square of the folded STS (known as the folded Cram??von Mises, or CvM, estimator) as estimators of the variance parameter of a stationary time series. In order to obtain results on the bias of the estimators, we provide a complete finite-sample analysis based on the mean-square error of the given estimators. Weights yielding first-order unbiased estimators are found in the area and CvM cases. Finally, we perform Monte Carlo simulations to test the efficacy of the new estimators on a test bed of stationary stochastic processes, including the first-order moving average and autoregressive processes and the waiting time process in a single-server Markovian queuing system.

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