SERIE RESEARCH mEmORRnOR ARMAX MODELS: ESTIMATION AND TESTING
نویسنده
چکیده
1. BASIC PROBABILITY THEORY 1.1 Measure-theoretical foundation of probability theory 1.2 Independence 1.3 Borel measurable functions 1.4 Mathematical expectatlon 1.5 Characteristic functions 1.6 Random functions 2. CONVERGENCE 2.1 Heak and strong convergence of random variables 2.2 Convergence of mathematical expectations 2.3 Convergence of distributions 2.4 Central limit theoreras 2.5 Further results on convergence of distributions and mathematical expectations, and laws of large numbers 2.6 Convergence of random functions 2.7 Uniform strong and weak laws of large numbers 3. INTRODUCTION TO CONDITIONING 3.1 Definition of conditional expectatlon 3.2. Basic properties of conditional expectations 3.3 Identification of conditional expectations PART 2: REGRESSION ANALYSIS UNDER INDEPENDENCE 4. NONLINEAR PARAMETRIC REGRESSION ANALYSIS 4.1 Nonlinear regression models and the nonlinear least squares estimator 4.2 Consistency and asymptotic normality: General theory 4.2.1 Consistency 4.2.2 Asymptotic normality 4.3 Consistency and asymptotic normality of nonlinear least squares estimators in the i.i.d. case 4.3.1 Consistency
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