Showing papers on "Population proportion published in 2014"
TL;DR: It has been shown that the proposed models are more efficient than Kim and Warde [12] stratified randomized response technique under some realistic conditions in both the cases of completely truthful reporting and that of notcompletely truthful reporting by the respondents.
Abstract: This paper addresses the problem of estimating the population proportion πS possessing the sensitive attribute using stratified random sampling. Two alternatives to Kim and Warde [12] stratified randomized response model have been proposed. The proposed models are based on extending Singh [28] models to the case of stratified random sampling. It has been shown that the proposed models are more efficient than Kim and Warde [12] stratified randomized response technique under some realistic conditions in both the cases of completely truthful reporting and that of not completely truthful reporting by the respondents. A practical problem with the use of optimum (Neyman) allocation has been pointed out. Thus, in practice, the use of either proportional allocation or equal allocation has been suggested while estimating proportion of a sensitive attribute using stratified randomized response sampling. Numerical illustrations and graphs are also given in support of the present study.
26 citations
TL;DR: In this article, improved classes of estimators have been proposed for estimating the population means of the study character using the information on auxiliary character and attribute in the presence of non-response on study character only and their properties are studied.
Abstract: In this paper, improved classes of estimators have been proposed for estimating the population means of the study character using the information on auxiliary character and attribute in the presence of non-response on study character only and their properties are studied. The conditions for attaining the minimum mean square error of the proposed classes of estimators are also given. Further, the problem has been extended to the two phase sampling when the population proportion and mean of auxiliary character are not known. Two wider classes of generalized estimators under these conditions have been proposed and their properties are studied. The expressions of optimum sample size for first phase, second phase and sub-sampling fraction have been obtained in case of fixed cost as well as for fixed precision. Theoretical and empirical studies have been made with relevant estimators to show the efficiency of the proposed classes of estimators with real data set from census handbook published by Government of India.
7 citations
TL;DR: A large-sample procedure for examining explained population variance in principal component analysis is discussed in this paper, which yields interval estimates for the proportions of variance accounted for by the considered for retention principal components.
Abstract: A large-sample procedure for examining explained population variance in principal component analysis is discussed. The method yields interval estimates for the proportions of variance accounted for by the considered for retention principal components. The obtained confidence intervals can be used to make decisions about the number of selected components, based on their performance in studied populations. The approach can also be employed for ascertaining group similarity or differences in percentage explained variance by the considered principal components. The procedure is illustrated with 2 examples.
6 citations
Book•
01 Jan 2014
TL;DR: In this paper, the authors make an informed decision about whether or not to buy a car or not based on a simple random sampling method, such as least squares regression or least square regression.
Abstract: Part 1: Getting the Information You Need 1 Data Collection 11 Introduction to the Practice of Statistics 12 Observational Studies versus Designed Experiments 13 Simple Random Sampling 14 Other Effective Sampling Methods 15 Bias in Sampling 16 The Design of Experiments Chapter 1 Review Chapter Test Making an Informed Decision: What College Should I Attend? Case Study: Chrysalises for Cash Part 2: Descriptive Statistics 2 Organizing and Summarizing Data 21 Organizing Qualitative Data 22 Organizing Quantitative Data: The Popular Displays 23 Graphical Misrepresentations of Data Chapter 2 Review Chapter Test Making an Informed Decision: Tables or Graphs? Case Study: The Day the Sky Roared 3 Numerically Summarizing Data 31 Measures of Central Tendency 32 Measures of Dispersion 33 Measures of Central Tendency and Dispersion from Grouped Data 34 Measures of Position and Outliers 35 The Five-Number Summary and Boxplots Chapter 3 Review Chapter Test Making an Informed Decision: What Car Should I Buy? Case Study: Who Was A Mourner? 4 Describing the Relation between Two Variables 41 Scatter Diagrams and Correlation 42 Least-Squares Regression 43 The Coefficient of Determination 44 Contingency Tables and Association Chapter 4 Review Chapter Test Making an Informed Decision: Relationships among Variables on a World Scale Case Study: Thomas Malthus, Population, and Subsistence Part 3: Probability and Probability Distributions 5 Probability 51 Probability Rules 52 The Addition Rule and Complements 53 Independence and the Multiplication Rule 54 Conditional Probability and the General Multiplication Rule 55 Counting Techniques 56 Putting It Together: Which Method Do I Use? Chapter 5 Review Chapter Test Making an Informed Decision: The Effects of Drinking and Driving Case Study: The Case of the Body in the Bag 6 Discrete Probability Distributions 61 Discrete Random Variables 62 The Binomial Probability Distribution Chapter 6 Review Chapter Test Making an Informed Decision: Should We Convict? Case Study: The Voyage of the St Andrew 7 The Normal Probability Distribution 71 Properties of the Normal Distribution 72 Applications of the Normal Distribution 73 Assessing Normality 74 The Normal Approximation to the Binomial Probability Distribution Chapter 7 Review Chapter Test Making an Informed Decision: Stock Picking Case Study: A Tale of Blood Chemistry and Health Part 4: Inference: From Samples to Population 8 Sampling Distributions 81 Distribution of the Sample Mean 82 Distribution of the Sample Proportion Chapter 8 Review Chapter Test Making an Informed Decision: How Much Time Do You Spend in a Day? Case Study: Sampling Distribution of the Median 9 Estimating the Value of a Parameter 91 Estimating a Population Proportion 92 Estimating a Population Mean 93 Putting It Together: Which Procedure Do I Use? Chapter 9 Review Chapter Test Making an Informed Decision: How Much Should I Spend for This House? Case Study: Fire-Safe Cigarettes 10 Hypothesis Tests Regarding a Parameter 101 The Language of Hypothesis Testing 102 Hypothesis Tests for a Population Proportion 103 Hypothesis Tests for a Population Mean 104 Putting It Together: Which Method Do I Use? Chapter 10 Review Chapter Test Making an Informed Decision: Selecting a Mutual Fund Case Study: How Old Is Stonehenge? 11 Inferences on Two Samples 111 Inference about Two Population Proportions 112 Inference about Two Means: Dependent Samples 113 Inference about Two Means: Independent Samples 114 Putting It Together: Which Method Do I Use? Chapter 11 Review Chapter Test Making an Informed Decision: Which Car Should I Buy? Case Study: Control in the Design of an Experiment 12 Inference on Categorical Data 121 Goodness-of-Fit Test 122 Tests for Independence and the Homogeneity of Proportions 123 Testing the Significance of the Least-Squares Regression Model 124 Confidence and Prediction Intervals Chapter 12 Review Chapter Test Making an Informed Decision: Benefits of College Case Study: Feeling Lucky? Well, Are You? Additional Topics on CD C1 Lines C2 Estimating a Population Standard Deviation C3 Hypothesis Tests for a Population Standard Deviation C4 Comparing Three or More Means (One-Way Analysis of Variance) Appendix A Tables Photo Credits Answers Index
6 citations
Journal Article•
TL;DR: In this article, the authors developed the Bayesian estimators of the population proportion of a stigmatized attribute using Kumaraswamy and generalized Beta prior distributions when data were obtained through the Randomized Response Technique (RRT) proposed by Kim and Warde.
Abstract: In this paper, we developed the Bayesian estimators of the population proportion of a stigmatized attribute using Kumaraswamy and Generalised Beta prior distributions when data were obtained through the Randomized Response Technique (RRT) proposed by Kim and Warde (15). We validated our newly developed Bayesian estimators for a wide range of the designed values of the population proportion at varying sample sizes. It was observed that our newly developed Bayesian estimators performed significantly better than the Bayesian estimator developed by Hussain and Shabbir (12) for relatively small as well as moderate sample sizes. However, the reverse was the case for very large sample sizes.
5 citations
01 Jan 2014
TL;DR: In this article, the authors discuss statistical inference where data collected will be viewed as a random sample from some population and the information so gathered from such a sample will then be used to conduct a Statistical estimation which basically comprises of determining an estimate of some parameter of the population as well as assessing the precision of such an estimate.
Abstract: In this chapter, we shall discuss statistical inference where data collected will be viewed as a random sample from some population. The information so gathered from such a sample will then be used to conduct a Statistical estimation which basically comprises of determining an estimate of some parameter of the population as well as assessing the precision of such an estimate. We present an example relating to contamination counts of a sample of 20 vaccines preserved with phenol.
1 citations
TL;DR: The prediction approach is used to define a new estimator that presents desirable efficiency properties that can be used to estimate a finite population proportion when there are missing values.
1 citations
01 Jan 2014
TL;DR: This chapter reviews how confidence intervals and tests of hypotheses are used to estimate prevalence and incidence from sample data, and how various measures of association based on sample proportions—the difference between two proportions, relative risk and the odds ratio—are used to identify risk factors.
Abstract: An important goal in clinical research is estimating the proportion of a population who has a particular disease or who will acquire the disease over a given period of time, and identifying factors that are associated with the occurrence of the disease. This chapter reviews how confidence intervals and tests of hypotheses are used to estimate prevalence and incidence from sample data, and how various measures of association based on sample proportions—the difference between two proportions, relative risk and the odds ratio—are used to identify risk factors.
1 citations
Posted Content•
TL;DR: In this paper, an almost unbiased estimator using known value of some population parameter(s) with known population proportion of an auxiliary variable is proposed, under simple random sampling without replacement (SRSWOR) scheme the expressions for bias and mean square error are derived.
Abstract: In this paper we have proposed an almost unbiased estimator using known value of some population parameter(s) with known population proportion of an auxiliary variable. A class of estimators is defined which includes [1], [2] and [3] estimators. Under simple random sampling without replacement (SRSWOR) scheme the expressions for bias and mean square error (MSE) are derived. Numerical illustrations are given in support of the present study. Key words: Auxiliary information, proportion, bias, mean square error, unbiased estimator.
TL;DR: In this article, an almost unbiased estimator using known value of some population parameter(s) with known population proportion of an auxiliary variable is proposed, which is a class of estimators which includes (1), (2) and (3) estimators.
Abstract: In this paper we have proposed an almost unbiased estimator using known value of some population parameter(s) with known population proportion of an auxiliary variable. A class of estimators is defined which includes (1), (2) and (3) estimators. Under simple random sampling without replacement (SRSWOR) scheme the expressions for bias and mean square error (MSE) are derived. Numerical illustrations are given in support of the present study.
TL;DR: In this paper, the authors use the idea of post-stratification based on the respondents' choice of a particular randomization device in order to estimate the population proportion of a sensitive characteristic.
Abstract: In this paper, we use the idea of post-stratification based on the respondents’ choice of a particular randomization device in order to estimate the population proportion of a sensitive characteristic. The proposed idea gives full freedom to the respondents and is expected to result in greater cooperation from them as well as to provide some increase in the relative efficiency of the newly proposed estimator.