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Quantization of Random Processes and Related Statistical ProblemsPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2006 (English)Doctoral thesis, comprehensive summary (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Umeå: Matematik och matematisk statistik , 2006. , 107 p.
##### Keyword [en]

scalar quantization, random process, rate, distortion, additive noise model, run-length encoding, compression, sample estimate, asymptotical normality
##### National Category

Probability Theory and Statistics
##### Identifiers

URN: urn:nbn:se:umu:diva-883ISBN: 91-7264-183-5OAI: oai:DiVA.org:umu-883DiVA: diva2:144878
##### Public defence

2006-10-27, MA 121, MIT-hus, Umeå University, SE-901 87, Umeå, Sweden, 10:15
##### Opponent

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##### Supervisors

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#####

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Available from: 2006-10-05 Created: 2006-10-05 Last updated: 2013-01-15Bibliographically approved
##### List of papers

In this thesis we study a scalar uniform and non-uniform quantization of random processes (or signals) in average case setting. Quantization (or discretization) of a signal is a standard task in all nalog/digital devices (e.g., digital recorders, remote sensors etc.). We evaluate the necessary memory capacity (or quantization rate) needed for quantized process realizations by exploiting the correlation structure of the model random process. The thesis consists of an introductory survey of the subject and related theory followed by four included papers (A-D).

In Paper A we develop a quantization coding method when quantization levels crossings by a process realization are used for its coding. Asymptotical behavior of mean quantization rate is investigated in terms of the correlation structure of the original process. For uniform and non-uniform quantization, we assume that the quantization cellwidth tends to zero and the number of quantization levels tends to infinity, respectively.

In Papers B and C we focus on an additive noise model for a quantized random process. Stochastic structures of asymptotic quantization errors are derived for some bounded and unbounded non-uniform quantizers when the number of quantization levels tends to infinity. The obtained results can be applied, for instance, to some optimization design problems for quantization levels.

Random signals are quantized at sampling points with further compression. In Paper D the concern is statistical inference for run-length encoding (RLE) method, one of the compression techniques, applied to quantized stationary Gaussian sequences. This compression method is widely used, for instance, in digital signal and image processing. First, we deal with mean RLE quantization rates for various probabilistic models. For a time series with unknown stochastic structure, we investigate asymptotic properties (e.g., asymptotic normality) of two estimates for the mean RLE quantization rate based on an observed sample when the sample size tends to infinity.

These results can be used in communication theory, signal processing, coding, and compression applications. Some examples and numerical experiments demonstrating applications of the obtained results for synthetic and real data are presented.

1. Uniform and non-uniform quantization of Gaussian processes$(function(){PrimeFaces.cw("OverlayPanel","overlay570797",{id:"formSmash:j_idt437:0:j_idt441",widgetVar:"overlay570797",target:"formSmash:j_idt437:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. Stochastic structure of asymptotic quantization errors$(function(){PrimeFaces.cw("OverlayPanel","overlay144875",{id:"formSmash:j_idt437:1:j_idt441",widgetVar:"overlay144875",target:"formSmash:j_idt437:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

3. Asymptotic quantization errors for unbounded quantizers$(function(){PrimeFaces.cw("OverlayPanel","overlay144876",{id:"formSmash:j_idt437:2:j_idt441",widgetVar:"overlay144876",target:"formSmash:j_idt437:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

4. Quantization of stationary random sequences and related statistical problems$(function(){PrimeFaces.cw("OverlayPanel","overlay144877",{id:"formSmash:j_idt437:3:j_idt441",widgetVar:"overlay144877",target:"formSmash:j_idt437:3:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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