Fifth Chapter - UC Davis Statistics
... The confidence coefficient in a confidence interval is the probability that an interval estimator encloses the population parameter. For a 90% confidence interval, the probability that an interval will enclose the population parameter is .90. In other words, if one took repeated samples and formed 9 ...
... The confidence coefficient in a confidence interval is the probability that an interval estimator encloses the population parameter. For a 90% confidence interval, the probability that an interval will enclose the population parameter is .90. In other words, if one took repeated samples and formed 9 ...
Recursive partitioning and Bayesian inference
... Remark: To ensure that this definition is meaningful, one must check that the two-stage procedure will in fact generate a well-defined conditional distribution with probability 1. To see this, first note that because the collection of all potential sets A on ΩX that can arise during Stage I is count ...
... Remark: To ensure that this definition is meaningful, one must check that the two-stage procedure will in fact generate a well-defined conditional distribution with probability 1. To see this, first note that because the collection of all potential sets A on ΩX that can arise during Stage I is count ...
Solutions to the Exercises
... is key in the original data and then press the correct button. (There might be a choice, one of which is when the divisor in the 'standard deviation' formula is n, the other is when the divisor is n - 1. Remember, in this course we use the second formula.) (a) You should have obtained s = 8.33, to t ...
... is key in the original data and then press the correct button. (There might be a choice, one of which is when the divisor in the 'standard deviation' formula is n, the other is when the divisor is n - 1. Remember, in this course we use the second formula.) (a) You should have obtained s = 8.33, to t ...
competing risks methods
... Kalbfleisch and Prentice, 2002; Lawless, 2003; Andersen et al., 2002 for further references). However, when all risks are present only T = min(T1 , . . . , Tk ) can be observed, together with C = j, such as T = T j , and an identifiable problem is found (Tsiatis, 1975; Cox and Oakes, 1984). F(t1 , . ...
... Kalbfleisch and Prentice, 2002; Lawless, 2003; Andersen et al., 2002 for further references). However, when all risks are present only T = min(T1 , . . . , Tk ) can be observed, together with C = j, such as T = T j , and an identifiable problem is found (Tsiatis, 1975; Cox and Oakes, 1984). F(t1 , . ...
PDF
... ~GMM! estimation of the regression involving the residuals generated by partialling+ The orthogonality condition for GMM expresses the moments of these residuals as functions of the parameters to be estimated+ The advantage of the two-step approach is that the numbers of equations and parameters in ...
... ~GMM! estimation of the regression involving the residuals generated by partialling+ The orthogonality condition for GMM expresses the moments of these residuals as functions of the parameters to be estimated+ The advantage of the two-step approach is that the numbers of equations and parameters in ...
Gerig, Thomas and Guillermo P. Zarate-De-Lara; (1976)Estimation in linear models using directionally minimax mean squared error."
... The purpose of this paper is to develop a meaningful criterion which can be used to derive possibly biased estimators which will be superior to 01S with 'respect to mean squared error. ...
... The purpose of this paper is to develop a meaningful criterion which can be used to derive possibly biased estimators which will be superior to 01S with 'respect to mean squared error. ...
Computational rank-based statistics
... discussed by Terpstra and McKean (2005), MINITAB’s RREGRESS command and RGLM. In addition, we compute the least squares estimate for the sake of comparison. The model for the data is a polynomial model of degree two. That is, ...
... discussed by Terpstra and McKean (2005), MINITAB’s RREGRESS command and RGLM. In addition, we compute the least squares estimate for the sake of comparison. The model for the data is a polynomial model of degree two. That is, ...
(with an application to the estimation of labor supply functions) James J.
... in its own right for three reasons: (a) an important variable utilized in the author's previous analysis, the labor market experience of women, was incorrectly coded by the primary data source, (b) the simple estimators discussed here allow for much more extensive testing of the maintained hypothese ...
... in its own right for three reasons: (a) an important variable utilized in the author's previous analysis, the labor market experience of women, was incorrectly coded by the primary data source, (b) the simple estimators discussed here allow for much more extensive testing of the maintained hypothese ...
Analysis of Environmental Data
... Formally, a probability distribution is the set of probabilities on a sample space or set of outcomes; it describes the set of probabilities for all possible outcomes of a random variable. Here, we will always be working with sample spaces that are numbers – the number or amount observed in some mea ...
... Formally, a probability distribution is the set of probabilities on a sample space or set of outcomes; it describes the set of probabilities for all possible outcomes of a random variable. Here, we will always be working with sample spaces that are numbers – the number or amount observed in some mea ...
Chapter 9: Two-Sample Inference
... Chapter 7 discussed methods of hypothesis testing about one-population parameters. Chapter 8 discussed methods of estimating population parameters from one sample using confidence intervals. This chapter will look at methods of confidence intervals and hypothesis testing for two populations. Since t ...
... Chapter 7 discussed methods of hypothesis testing about one-population parameters. Chapter 8 discussed methods of estimating population parameters from one sample using confidence intervals. This chapter will look at methods of confidence intervals and hypothesis testing for two populations. Since t ...
German tank problem
In the statistical theory of estimation, the problem of estimating the maximum of a discrete uniform distribution from sampling without replacement is known in English as the German tank problem, due to its application in World War II to the estimation of the number of German tanks.The analyses illustrate the difference between frequentist inference and Bayesian inference.Estimating the population maximum based on a single sample yields divergent results, while the estimation based on multiple samples is an instructive practical estimation question whose answer is simple but not obvious.