Browsing by Subject "Cross-validation"
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Item A Cross-Validated Approach Towards Identifying the Unique and Cumulative Contributions of Child and Family Factors Predictive of Speech-Language Therapy Start Time(2021-08) Elmquist, MarianneLanguage is an important milestone in early childhood and paves the way for later achievement and outcomes. Early identification and communication-focused interventions can provide additional environmental supports to foster language development for children who face challenges in acquiring language. To accurately identify and predict who may be likely to receive speech/language services, it is crucial to understand the unique contribution and the cumulative effect of factors that best predict speech-language service status and service receipt start time. Using data from the Early Childhood Longitudinal Study – Birth Cohort (ELCS-B), the current study sought to identify, within a bio-ecobehavioral framework, the unique and combined contribution of child and family factors predictive of speech-language therapy (SLT) start time. Multinomial logistic regression was used to evaluate child and family factors predictive of SLT start time. In addition, k-fold cross-validation was performed to assess generalizability. The final model accurately predicted SLT start time 62% of the time and only included child-related factors. The presence of an intellectual and developmental disability (IDD), prelinguistic performance, cognitive growth, and the number of words said at 24-months all provided a unique contribution in predicting SLT start time.Item Estimation of conditional average treatment effects(2014-07) Rolling, Craig AnthonyResearchers often believe that a treatment's effect on a response may be heterogeneous with respect to certain baseline covariates. This is an important premise of personalized medicine and direct marketing. Within a given set of regression models or machine learning algorithms, those that best estimate the regression function may not be best for estimating the effect of a treatment; therefore, there is a need for methods of model selection targeted to treatment effect estimation. In this thesis, we demonstrate an application of the focused information criterion (FIC) for model selection in this setting and develop a treatment effect cross-validation (TECV) aimed at minimizing treatment effect estimation errors. Theoretically, TECV possesses a model selection consistency property when the data splitting ratio is properly chosen. Practically, TECV has the flexibility to compare different types of models and estimation procedures.In the usual regression settings, it is well established that model averaging (or more generally, model combining) frequently produces substantial performance gains over selecting a single model, and the same is true for the goal of treatment effect estimation. We develop a model combination method (TEEM) that properly weights each model based on its (estimated) accuracy for estimating treatment effects. When the baseline covariate is one-dimensional, the TEEM algorithm automatically produces a treatment effect estimate that converges at almost the same rate as the best model in a candidate set.We illustrate the methods of FIC, TECV, and TEEM with simulation studies, data from a clinical trial comparing treatments of patients with HIV, and a benchmark public policy dataset from a work skills training program. The examples show that the methods developed in this thesis often exhibit good performance for the important goal of estimating treatment effects conditional on covariates.Item Mplus syntax for double cross-validation using latent class analysis (LCA) and comparing outcomes across classes(2018) Merians, Addie N; Baker, Majel R; Frazier, Patricia A; Lust, Katherine; pfraz@umn.edu; Frazier, Patricia; Department of PsychologyMplus syntax for double cross-validation using latent class analysis (LCA) and comparing outcomes across classes. This includes exploratory LCA to identify a best fitting model, cross-validating the model in separate halves of the study sample, and comparing outcomes (i.e., mental health, physical health, alcohol consequences, and GPA) across latent classes using a bias-adjusted, three-step analysis for comparing outcomes across latent classes.Item Uncertainty in Economic Optimum Nitrogen Rate and Accuracy of Drone Hyperspectral Imaging for Precision Nitrogen Management in Maize(2021-06) Nigon, TylerOver the past century, the global nitrogen cycle has been substantially altered by nitrogen fixation via the Haber-Bosch process. This fixed nitrogen is primarily used as fertilizer, ultimately supporting food, fuel, and fiber production for the ever-growing global human population. In the United States, maize production uses far more Haber-Bosch nitrogen than any other activity. Nitrogen fertilizer is necessary to achieve optimal profits, but also contributes to unintended environmental pollution, especially when applied in excess. A great deal of research has been conducted over the past several decades to improve maize nitrogen fertilizer recommendations. However, recommendations are still less accurate than necessary at the field level to successfully balance the resulting economic and environmental tradeoffs. The overarching goal of this research was to improve the understanding and extensibility of precision nitrogen fertilizer recommendations for maize. This goal was addressed by focusing on two areas that currently leads to much of the uncertainty around recommendations: i) uncertainty around the modeled economic optimal nitrogen rate derived from yield response data and ii) quality control standards for developing and implementing remote sensing-based models for predicting in-season crop nitrogen status. The focal point of each of these research areas is the spatial and temporal variation that exists in nitrogen requirements across space and from season to season. The results from this research show there was substantial variability in the modeled economic optimal nitrogen rates for several sites across Minnesota (90% confidence intervals ranged from 42 to 485 kg ha-1). Any regional economic or social analyses are only as reliable as this range of uncertainty around the modeled optimal rate, so caution must be taken to avoid misguided policy recommendations. Hyperspectral imaging was used to accurately predict early-season maize nitrogen uptake (relative RMSE < 24%). Optimizing the image processing protocol improved accuracy further, but it remains a challenge to predict the optimal nitrogen rate from early-season nitrogen status metrics such as nitrogen uptake. Doing so is a necessary step towards estimating nitrogen need and applying nitrogen at the most suitable rates and times so nitrogen recovery is maximized and nutrient loss is minimized.