Spatial auto‐correlation and auto‐regressive models estimation from sample survey data

2020 ◽  
Vol 62 (6) ◽  
pp. 1494-1507
Author(s):  
Roberto Benedetti ◽  
Thomas Suesse ◽  
Federica Piersimoni
Keyword(s):  
Survey Data ◽  
Sample Survey ◽  
Auto Correlation ◽  
Sample Survey Data ◽  
Auto Regressive

Restricted quasi-score estimating functions for sample survey data

2004 ◽  
Vol 41 (A) ◽  
pp. 119-130
Author(s):  
Y.-X. Lin ◽  
D. Steel ◽  
R. L Chambers
Keyword(s):  
Maximum Likelihood ◽  
Survey Data ◽  
Sample Survey ◽  
Likelihood Method ◽  
Sample Surveys ◽  
Estimating Functions ◽  
Limit Theory ◽  
Normal Probability ◽  
Probability Structure ◽  
Sample Survey Data

This paper applies the theory of the quasi-likelihood method to model-based inference for sample surveys. Currently, much of the theory related to sample surveys is based on the theory of maximum likelihood. The maximum likelihood approach is available only when the full probability structure of the survey data is known. However, this knowledge is rarely available in practice. Based on central limit theory, statisticians are often willing to accept the assumption that data have, say, a normal probability structure. However, such an assumption may not be reasonable in many situations in which sample surveys are used. We establish a framework for sample surveys which is less dependent on the exact underlying probability structure using the quasi-likelihood method.

scholarly journals Prevalence and types of sexual inactivity in Britain: analyses of national cross-sectional probability survey data

BMJ Open ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. e030708 ◽  
Author(s):  
Peter Ueda ◽  
Catherine H Mercer
Keyword(s):  
Survey Data ◽  
Sexual Experience ◽  
Early Adulthood ◽  
Well Being ◽  
Anal Intercourse ◽  
Sample Survey ◽  
Cross Sectional ◽  
Sample Survey Data ◽  
And Gender ◽  
The Life Course

ObjectivesTo examine how prevalence and context of sexual inactivity varies across the life-course, assess dissatisfaction with sex life among those who are sexually inactive and identify associations with factors that may represent reasons for sexual inactivity.DesignAnalysis of cross-sectional probability sample survey data.SettingBritish general population.Participants14 623 participants (n men: 6045 unweighted, 7245 weighted), aged 16–74 years, of the third National Survey of Sexual Attitudes and Lifestyles (Natsal-3), undertaken in 2010–2012.Main outcome measuresSexual inactivity, defined as not reporting oral, vaginal or anal intercourse in the past year and further categorised into those who were sexually inexperienced (had never had sex), single or in a relationship.ResultsOverall, 15.9% (weighted n 1155/7245) of men and 22.2% (1646/7410) of women were sexually inactive (p<0.001). The proportion of sexually inactive individuals who were sexually inexperienced was larger among men than women (26.3% (304/1155) vs 16.3% (268/1646)), while the proportion who were singles was larger among women (49.8% (820/1646) vs 40.4% (467/1155)). Sexual inexperience was the most common type of sexual inactivity in early adulthood, with this declining with age. A minority of those who had sexual experience but were sexually inactive—34.8% (293/842) of men and 23.6% (319/1349) of women—reported being dissatisfied with their sex lives. Associations with sexual inactivity was observed for a range of sociodemographics and sexual behaviours/attitudes, for example, religion, ethnicity, Body mass index, height, employment status and index of multiple deprivation; these associations varied by type of sexual inactivity and gender.ConclusionsWhile sex is important for well-being, a non-negligible proportion of the population at all ages are sexually inactive, yet many are not dissatisfied with their situation, with implications for sex and relationship counselling.

Sign in / Sign up

Export Citation Format

Share Document