FQM365 - Artículoshttps://hdl.handle.net/10481/683012024-03-29T10:19:31Z2024-03-29T10:19:31ZQuantile Interval Estimation in Finite Population Using a Multivariate Ratio EstimatorRueda García, María Del MarArcos Cebrián, AntonioArtés Rodríguez, Eva Maríahttps://hdl.handle.net/10481/881252024-02-04T16:03:54ZQuantile Interval Estimation in Finite Population Using a Multivariate Ratio Estimator
Rueda García, María Del Mar; Arcos Cebrián, Antonio; Artés Rodríguez, Eva María
A new method to derive confidence intervals for quantiles in a finite population is presented
This method uses multi-auxiliary information throuoh a multi-variate ratio type estimator of the population
distribution function.
Improvement on Estimating Quantiles in Finite Population Using Indirect Methods of EstimationRueda García, María Del MarArcos Cebrián, AntonioArtés Rodríguez, Eva Maríahttps://hdl.handle.net/10481/881232024-02-04T16:01:20ZImprovement on Estimating Quantiles in Finite Population Using Indirect Methods of Estimation
Rueda García, María Del Mar; Arcos Cebrián, Antonio; Artés Rodríguez, Eva María
New methods for estimating confidence limits for quantiles
in a finite population are proposed. These methods use auxiliary information
through the ratio, difference and regression estimator of the
population distribution function. They may be applied to any type of
sampling. Simulation studies based of two real populations show that
the methods proposed in this paper can be considerably more efficient
than the customary classic method.
Enhancing estimation methods for integrating probability and non-probability survey samples with machine-learning techniques. An application to a Survey on the impact of the COVID-19 pandemic in SpainRueda García, María del MarPasadas del Amo, SaraCobo Rodríguez, BeatrizCastro Martín, LuisFerri García, Ramónhttps://hdl.handle.net/10481/875162024-01-29T13:04:31ZEnhancing estimation methods for integrating probability and non-probability survey samples with machine-learning techniques. An application to a Survey on the impact of the COVID-19 pandemic in Spain
Rueda García, María del Mar; Pasadas del Amo, Sara; Cobo Rodríguez, Beatriz; Castro Martín, Luis; Ferri García, Ramón
Web surveys have replaced Face-to-Face and computer assisted telephone interviewing
(CATI) as the main mode of data collection in most countries. This trend
was reinforced as a consequence of COVID-19 pandemic-related restrictions.
However, this mode still faces significant limitations in obtaining probabilitybased
samples of the general population. For this reason, most web surveys rely
on nonprobability survey designs. Whereas probability-based designs continue
to be the gold standard in survey sampling, nonprobability web surveys may
still prove useful in some situations. For instance, when small subpopulations
are the group under study and probability sampling is unlikely to meet sample
size requirements, complementing a small probability sample with a larger
nonprobability one may improve the efficiency of the estimates. Nonprobability
samples may also be designed as a mean for compensating for known biases in
probability-basedweb survey samples by purposely targeting respondent profiles
that tend to be underrepresented in these surveys. This is the case in the Survey
on the impact of the COVID-19 pandemic in Spain (ESPACOV) that motivates
this paper. In this paper, we propose a methodology for combining probability
and nonprobabilityweb-based survey sampleswith the help ofmachine-learning
techniques. We then assess the efficiency of the resulting estimates by comparing
them with other strategies that have been used before. Our simulation study
and the application of the proposed estimation method to the second wave of the
ESPACOV Survey allow us to conclude that this is the best option for reducing
the biases observed in our data.
Applied Mathematics and Information SciencesRueda García, María Del MarArcos Cebrián, AntonioCobo Rodríguez, Beatrizhttps://hdl.handle.net/10481/875072024-01-29T12:45:44ZApplied Mathematics and Information Sciences
Rueda García, María Del Mar; Arcos Cebrián, Antonio; Cobo Rodríguez, Beatriz
The methodology of randomized response has advanced considerably in recent years. Nevertheless, to date all the proposed
estimators with randomized response techniques have been based on the hypothesis of the availability of a unique and complete list of
units forming the target population to be used as a sampling frame. In this paper, we present a new procedure aimed at determining
a population total using a model of randomized response when data are obtained from two frames. We introduce different ways of
combining estimates obtained from the different frames and propose unbiased estimators, with an analytic expression for their variances.
Estimates for the variances are also obtained, applying analytical formulas such as those based on resampling technologies. A simulation
study illustrates the behaviour of the estimator using diverse randomization devices.
Use of the Therapy App Prescinde for Increasing Adherence to Smoking Cessation TreatmentLópez Torrecillas, FranciscaRamírez-Uclés, IsabelRueda García, María Del MarCobo Rodríguez, BeatrizCastro Martín, LuisUrrea Castaño, ArantxaMuñoz López, Lucashttps://hdl.handle.net/10481/874872024-01-29T11:30:35ZUse of the Therapy App Prescinde for Increasing Adherence to Smoking Cessation Treatment
López Torrecillas, Francisca; Ramírez-Uclés, Isabel; Rueda García, María Del Mar; Cobo Rodríguez, Beatriz; Castro Martín, Luis; Urrea Castaño, Arantxa; Muñoz López, Lucas
Tobacco use poses major health risks and is a major contributor to causes of death worldwide.
Mobile phone-based cessation apps for this substance are gaining popularity, often used as a
component of traditional interventions. This study aimed to analyze adherence to an intervention
using a mobile phone application (App-therapy Prescinde (v1)) as a function of sociodemographic
variables (age, gender, educational level, and profession) as well as the primary activities supported
by the app (reducing tobacco or cannabis use and increasing physical exercise). The participants were
recruited through the web pages of the Occupational Risk Prevention Service and the Psychology
Clinic of the University of Granada during the COVID-19 confinement period. The application’s
contents include three components (self-report, motivational phrases, and goal setting). Our findings
indicate that being male, being aged between 26 and 62, having a high school education, and being
unemployed increase the likelihood of adherence to the Prescinde therapy app three months after
usage. Our findings highlight the importance of developing new therapeutic approaches and conducting
in-depth studies on the factors associated with adherence to tobacco cessation and cannabis
cessation treatments via mobile phone applications.