## Statistical Signal Processing: Detection, Estimation, and Time Series AnalysisThis book embraces the many mathematical procedures that engineers and statisticians use to draw inference from imperfect or incomplete measurements.This book presents the fundamental ideas in statistical signal processing along four distinct lines: mathematical and statistical preliminaries; decision theory; estimation theory; and time series analysis. |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Common terms and phrases

algorithm analysis ARMA assume autoregressive Bayes risk binary Chapter coefficients columns compute conditional mean correlation matrix covariance matrix data matrix decision rule decomposition denote a random density function detector diagonal eigenvalues eigenvector Example false alarm probability Fisher information matrix formula Gram matrix hypothesis testing illustrated in Figure impulse response inverse Kalman filter lattice least squares solution Let•s likelihood ratio linear prediction linear statistical model linearly independent log likelihood low-rank matched filter matrix H maximum likelihood estimate measurement minimax minimizes minimum mean-squared error modes multivariate normal Neyman-Pearson Neyman-Pearson lemma noise normal random vector orthogonal subspace parameter polynomial posterior prediction error predictor problem produces pseudoinverse quadratic form quantizer random sample random variables random vector recursions result scalar score function Section sequence Show signal-to-noise ratio singular value solve sufficient statistic theorem theory transformation unbiased estimator unknown versus H written zero