Biostatistics 1: Basic Concepts

2010 ◽  
pp. 3-11
Author(s):  
M. Hassan Murad ◽  
Qian Shi
Keyword(s):  
Odds Ratio ◽  
Sampling Error ◽  
Interval Estimation ◽  
Point Estimation ◽  
Descriptive Data ◽  
Sample Size Calculations ◽  
Measures Of Dispersion ◽  
Basic Concepts ◽  
Error Bias

Chapter 1 reviews basic concepts of biostatistics. Topics include descriptive data, probability and odds, estimation and sampling error, hypothesis testing, and power and sample size calculations. The discussion of descriptive data includes types of data (discrete vs continuous and nominal vs ordinal), central tendency (mean, median, and mode), skewed distributions, and measures of dispersion (range, variance, standard deviation). Probability and odds are broken down into laws of probability, odds, odds ratio, relative risk, and probability distribution. The examination of estimation and sampling error covers concepts such as random error, bias, standard error, point estimation, and interval estimation.

scholarly journals Survival estimation for singly type one censored sample based on generalized Rayleigh distribution

2014 ◽  
Vol 11 (2) ◽  
pp. 193-201
Author(s):  
Baghdad Science Journal
Keyword(s):  
Current Model ◽  
Survival Function ◽  
Information Matrix ◽  
Interval Estimation ◽  
Real Data ◽  
Rayleigh Distribution ◽  
Point Estimation ◽  
Distribution Model ◽  
Unknown Parameters ◽  
Generalized Rayleigh Distribution

This paper interest to estimation the unknown parameters for generalized Rayleigh distribution model based on censored samples of singly type one . In this paper the probability density function for generalized Rayleigh is defined with its properties . The maximum likelihood estimator method is used to derive the point estimation for all unknown parameters based on iterative method , as Newton – Raphson method , then derive confidence interval estimation which based on Fisher information matrix . Finally , testing whether the current model ( GRD ) fits to a set of real data , then compute the survival function and hazard function for this real data.

1. Introduction

2021 ◽  
pp. 1-26
Author(s):  
Andrew L-T Choo
Keyword(s):  
Human Rights ◽  
Direct Evidence ◽  
Circumstantial Evidence ◽  
Law Reform ◽  
Human Rights Act ◽  
Human Rights Act 1998 ◽  
Basic Concepts ◽  
The Law ◽  
Law Of Evidence

Chapter 1 examines a number of basic concepts and distinctions in the law of evidence. It covers facts in issue and collateral facts; relevance, admissibility, and weight; direct evidence and circumstantial evidence; testimonial evidence and real evidence; the allocation of responsibility; exclusionary rules and exclusionary discretions; free(r) proof; issues in criminal evidence; civil evidence and criminal evidence; the implications of trial by jury; summary trials; law reform; and the implications of the Human Rights Act 1998. This chapter also presents an overview of the subsequent chapters.

1. Introduction

Evidence ◽  
2018 ◽  
Author(s):  
Andrew L-T Choo
Keyword(s):  
Human Rights ◽  
Direct Evidence ◽  
Circumstantial Evidence ◽  
Law Reform ◽  
Human Rights Act ◽  
Human Rights Act 1998 ◽  
Basic Concepts ◽  
The Law ◽  
Law Of Evidence

Chapter 1 examines a number of basic concepts and distinctions in the law of evidence. It covers facts in issue and collateral facts; relevance, admissibility, and weight; direct evidence and circumstantial evidence; testimonial evidence and real evidence; the allocation of responsibility; exclusionary rules and exclusionary discretions; free(r) proof; issues in criminal evidence; civil evidence and criminal evidence; the implications of trial by jury; summary trials; law reform; and the implications of the Human Rights Act 1998. This chapter also presents an overview of the subsequent chapters.

Estimating Interval of the Number of Errors for Embedded Software Development Projects

2014 ◽  
Vol 2 (3) ◽  
pp. 40-50 ◽  
Author(s):  
Kazunori Iwata ◽  
Toyoshiro Nakasima ◽  
Yoshiyuki Anan ◽  
Naohiro Ishii
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Software Development ◽  
Previous Investigation ◽  
Interval Estimation ◽  
Embedded Software ◽  
Point Estimation ◽  
Support Vector ◽  
Development Projects ◽  
Artificial Neural Network Ann

Previous investigation focused on the prediction of total and errors for embedded software development projects using an artificial neural network (ANN). However, methods using ANNs have reached their improvement limits, since an appropriate value is estimated using what is known as point estimation in statistics. This paper proposes a method for predicting the number of errors for embedded software development projects using interval estimation provided by a support vector machine and ANN.

scholarly journals Catalytic prior distributions with application to generalized linear models

2020 ◽  
Vol 117 (22) ◽  
pp. 12004-12010
Author(s):  
Dongming Huang ◽  
Nathan Stein ◽  
Donald B. Rubin ◽  
S. C. Kou
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Synthetic Data ◽  
Interval Estimation ◽  
Likelihood Estimation ◽  
Predictive Distribution ◽  
Point Estimation ◽  
Tuning Parameter ◽  
Working Model ◽  
Coverage Accuracy

A catalytic prior distribution is designed to stabilize a high-dimensional “working model” by shrinking it toward a “simplified model.” The shrinkage is achieved by supplementing the observed data with a small amount of “synthetic data” generated from a predictive distribution under the simpler model. We apply this framework to generalized linear models, where we propose various strategies for the specification of a tuning parameter governing the degree of shrinkage and study resultant theoretical properties. In simulations, the resulting posterior estimation using such a catalytic prior outperforms maximum likelihood estimation from the working model and is generally comparable with or superior to existing competitive methods in terms of frequentist prediction accuracy of point estimation and coverage accuracy of interval estimation. The catalytic priors have simple interpretations and are easy to formulate.

Sign in / Sign up

Export Citation Format

Share Document