NAG Library Chapter Contents

g07 – Univariate Estimation


g07 Chapter Introduction – a description of the Chapter and an overview of the algorithms available

Function
Name
Mark of
Introduction

Purpose
g07aac
Example Text
Example Data
7 nag_binomial_ci
Computes confidence interval for the parameter of a binomial distribution
g07abc
Example Text
Example Data
7 nag_poisson_ci
Computes confidence interval for the parameter of a Poisson distribution
g07bbc
Example Text
Example Data
7 nag_censored_normal
Computes maximum likelihood estimates for parameters of the Normal distribution from grouped and/or censored data
g07bec
Example Text
Example Data
7 nag_estim_weibull
Computes maximum likelihood estimates for parameters of the Weibull distribution
g07bfc
Example Text
Example Data
9 nag_estim_gen_pareto
Estimates parameter values of the generalized Pareto distribution
g07cac
Example Text
Example Data
4 nag_2_sample_t_test
Computes t-test statistic for a difference in means between two Normal populations, confidence interval
g07dac
Example Text
Example Data
3 nag_median_1var
Robust estimation, median, median absolute deviation, robust standard deviation
g07dbc
Example Text
Example Data
4 nag_robust_m_estim_1var
Robust estimation, M-estimates for location and scale parameters, standard weight functions
g07dcc
Example Text
Example Data
7 nag_robust_m_estim_1var_usr
Robust estimation, M-estimates for location and scale parameters, user-defined weight functions
g07ddc
Example Text
Example Data
4 nag_robust_trimmed_1var
Trimmed and winsorized mean of a sample with estimates of the variances of the two means
g07eac
Example Text
Example Data
7 nag_rank_ci_1var
Robust confidence intervals, one-sample
g07ebc
Example Text
Example Data
7 nag_rank_ci_2var
Robust confidence intervals, two-sample
g07gac
Example Text
Example Data
23 nag_outlier_peirce
Outlier detection using method of Peirce, raw data or single variance supplied
g07gbc
Example Text
Example Data
23 nag_outlier_peirce_two_var
Outlier detection using method of Peirce, two variances supplied
© The Numerical Algorithms Group Ltd, Oxford, UK. 2017