Title :
A statistical analysis of adaptive quantization based on causal past
Author_Institution :
Dept. of Stat., California Univ., Berkeley, CA, USA
Abstract :
A statistical estimation framework is proposed for adaptive quantization based on causal past. Different estimation methods are given for the marginal density based on the quantized sample. For a stationary and ergodic source process, if its marginal density is in a parametric family with a dimension less than the quantization level, then “adaptation” can be achieved when the sample size is large, i.e., the marginal density can be estimated consistently
Keywords :
adaptive signal processing; encoding; estimation theory; quantisation (signal); statistical analysis; adaptive quantization; causal past; dimension; encoding; ergodic source process; estimation methods; marginal density; parametric family; quantization level; quantized sample; sample size; stationary source process; statistical analysis; statistical estimation; Equations; Gaussian processes; Maximum likelihood estimation; Monte Carlo methods; Quantization; Statistical analysis; Stochastic processes; Testing;
Conference_Titel :
Information Theory, 1995. Proceedings., 1995 IEEE International Symposium on
Conference_Location :
Whistler, BC
Print_ISBN :
0-7803-2453-6
DOI :
10.1109/ISIT.1995.550362
