Pages that link to "Item:Q5146049"
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The following pages link to Doubly Robust Inference With Nonprobability Survey Samples (Q5146049):
Displaying 27 items.
- Inference for nonprobability samples (Q1702506) (← links)
- On making valid inferences by integrating data from surveys and other sources (Q2040672) (← links)
- Model-assisted SCAD calibration for non-probability samples (Q2077434) (← links)
- Weight smoothing for nonprobability surveys (Q2084713) (← links)
- Modeling qualitative outcomes by supplementing participant data with general population data: a new and more versatile approach (Q2121824) (← links)
- Robust Bayesian inference for big data: combining sensor-based records with traditional survey data (Q2154206) (← links)
- Rejoinder on “A selective review of statistical methods using calibration information from similar studies” (Q5880127) (← links)
- Matrix completion under complex survey sampling (Q6046052) (← links)
- General purpose multiply robust data integration procedures for handling nonprobability samples (Q6049756) (← links)
- Pseudo empirical likelihood inference for nonprobability survey samples (Q6059416) (← links)
- Statistical inference from finite population samples: A critical review of frequentist and Bayesian approaches (Q6059417) (← links)
- Data Integration by Combining Big Data and Survey Sample Data for Finite Population Inference (Q6066747) (← links)
- Calibrated regression estimation using empirical likelihood under data fusion (Q6071722) (← links)
- Estimating the Area under the ROC Curve When Transporting a Prediction Model to a Target Population (Q6079722) (← links)
- Addressing selection bias and measurement error in COVID-19 case count data using auxiliary information (Q6138611) (← links)
- Pretest estimation in combining probability and non-probability samples (Q6170605) (← links)
- A dual-frame approach for estimation with respondent-driven samples (Q6175321) (← links)
- Applying multilevel regression weighting when only population margins are available (Q6181889) (← links)
- Enhancing estimation methods for integrating probability and nonprobability survey samples with machine-learning techniques. An application to a survey on the impact of the COVID-19 pandemic in Spain (Q6550310) (← links)
- Integrating rather than collecting: statistical matching in the data flood era (Q6579417) (← links)
- Integrating probability and big non-probability samples data to produce official statistics (Q6580650) (← links)
- On using a non-probability sample for the estimation of population parameters (Q6582509) (← links)
- Kernel weighting for blending probability and non-probability survey samples (Q6583116) (← links)
- Bayesian estimation methods for survey data with potential applications to health disparities research (Q6604345) (← links)
- Adjusted logistic propensity weighting methods for population inference using nonprobability epidemiologic cohorts (Q6628023) (← links)
- Assessing COVID-19 Prevalence in Austria with Infection Surveys and Case Count Data as Auxiliary Information (Q6631667) (← links)
- Information projection approach to smoothed propensity score weighting for handling selection bias under missing at random (Q6664139) (← links)
