Download Corso dottorato di Statistical Inference docente Daniela ICHIM (ISTAT)

Corso dottorato di Statistical Inference
docente Daniela ICHIM (ISTAT)
libro di testo: Probability and Statistics
Jeffrey Rosenthal and Michael Evans
vedi
http://www.amazon.com/Probability-Statistics-The-ScienceUncertainty/dp/1429224622
Programma del corso basato sul testo
"Probability and Statistics" di J. Rosenthal e M. Evans, Palgrave - Mc
Millan
LEZIONE 1
4 Sampling Distributions and Limits
4.1 Sampling Distributions
4.5 Monte Carlo Approximations
LEZIONE 2
4.6 Normal Distribution Theory
4.6.1 The Chi-Squared Distribution
4.6.2 The t Distribution
4.6.3 The F Distribution
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LEZIONE 3
5 Statistical Inference
5.1 Why Do We Need Statistics?
5.2 Inference Using a Probability Model
5.3 Statistical Models
5.4 Data Collection
5.4.1 Finite Populations
5.4.2 Simple Random Sampling
5.4.3 Histograms
5.4.4 Survey Sampling
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LEZIONE 4
5.5 Some Basic Inferences
5.5.1 Descriptive Statistics
5.5.2 Plotting Data
5.5.3 Types of Inferences
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LEZIONE 5
6 Likelihood Inference
6.1 The Likelihood Function
6.1.1 Sufficient Statistics
6.2 Maximum Likelihood Estimation
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LEZIONE 6
6.2.1 Computation of the MLE
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6.2.2 The Multidimensional Case (Advanced)
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6.3 Inferences Based on the MLE
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6.3.1 Standard Errors, Bias, and Consistency
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LEZIONE 7
6.3.2 Confidence Intervals
6.3.3 Testing Hypotheses and P-Values
6.3.4 Inferences for the Variance
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LEZIONE 8
6.3.5 Sample-Size Calculations: Confidence Intervals
6.3.6 Sample-Size Calculations: Power
6.4 Distribution-Free Methods
6.4.1 Method of Moments
6.4.2 Bootstrapping
6.4.3 The Sign Statistic and Inferences about Quantiles
6.5 Asymptotics for the MLE (Advanced)
LEZIONE 9
8 Optimal Inferences
8.1 Optimal Unbiased Estimation
8.1.1 The Rao-Blackwell Theorem and Rao-Blackwellization
8.1.2 Completeness and the Lehmann-Scheff
Theorem
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8.1.3 The Crame-Rao Inequality (Advanced)
LEZIONE 10
8.2 Optimal Hypothesis Testing
8.2.1 The Power Function of a Test
8.2.2 Type I and Type II Errors
8.2.3 Rejection Regions and Test Functions
8.2.4 The Neyman-Pearson Theorem
8.2.5 Likelihood Ratio Tests (Advanced)
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LEZIONE 11
9 Model Checking 479
9.1 Checking the Sampling Model
9.1.1 Residual and Probability Plots
9.1.2 The Chi-Squared Goodness of Fit Test
9.1.3 Prediction and Cross-Validation
9.1.4 What Do We Do When a Model Fails?
9.2 Checking for Prior-Data Conflict
9.3 The Problem with Multiple Checks
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