Nonparametric Model Research Articles

Construction of A Geographically Weighted Nonparametric Regression Model Fit Test

One of the approach of Geographically Weighted Regression (GWR) models is the Geographically Weighted Nonparametric Regression (GWNR) has more parameters than the GWR model. Models with more parameters usually have better match values, which is an advantage, while models with fewer parameters have the advantage of being easier to use and interpret. However, a model with more parameters should be used if it is proven to be significantly superior. Therefore, the purpose of this study was to develop a hypothesis test of goodness of fit test for GWNR model. The goodness of fit test was performed for the real data. We found that the GWNR model was more suitable than the mixed nonparametric regression model. Some highlights of the proposed method are:• A new model for GWR to overcome the unknown regression function by using mixed estimator spline truncated and fourier series at nonparametric regression• Goodness of fit for GWNR to testing the model fit between the mixed nonparametric regression model and GWNR• Applied goodness of fit test to poverty data in Sulawesi Island and infant mortality in East Java

Time-varying impacts of green credit on carbon productivity in China: New evidence from a non-parametric panel data model

In the context of global climate change threatening human survival, and in a post-pandemic era that advocates for a global green and low-carbon economic recovery, conducting an in-depth analysis to assess whether green finance can effectively support low-carbon economic development from a dynamic perspective is crucial. Unlike existing research, which focuses solely on the average effects of green credit (GC) on carbon productivity (CP), we introduce a non-parametric panel data model to investigate GC's impact on CP across 30 provinces in China from 2003 to 2021, verifying a significant time-varying effect. Specifically, during the first phase (2003–2008), GC negatively impacted CP. In the second phase (2009–2014), this negative influence gradually diminished and transformed into a positive effect. In the third phase (2015–2021), GC continued to positively influence CP, although this effect became insignificant during the pandemic. Further subgroup analysis reveals that in the regions with low environmental regulations, GC did not significantly boost CP throughout the sample period. In contrast, in the regions with high environmental regulations, GC's positive effect persisted in the mid to late stages of the sample period. Additionally, compared to the regions with low levels of marketization, the impact of GC on CP was more pronounced in highly marketized regions. This indicates that the promoting effect of GC on CP depends on strong support from environmental regulations and well-functioning market mechanisms. By adopting a non-parametric approach, this study reveals variations in the impact of GC on CP across different stages and under the influence of the pandemic shock, offering new insights into the relationship between GC and China's CP.

Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses

Cytometry is the standard multi-parameter assay for measuring single cell phenotype and functionality. It is commonly used for quantifying the relative frequencies of cell subsets in blood and disaggregated tissues. A typical analysis of cytometry data involves cell classification—that is, the identification of cell subgroups in the sample—and comparisons of the cell subgroups across samples or conditions. While modern experiments often necessitate the collection and processing of samples in multiple batches, analysis of cytometry data across batches is challenging because differences across samples may occur due to either true biological variation or technical reasons such as antibody lot effects or instrument optics across batches. Thus a critical step in comparative analyses of multi-sample cytometry data—yet missing in existing automated methods for analyzing such data—is cross-sample calibration, whose goal is to align corresponding cell subsets across multiple samples in the presence of technical variations, so that biological variations can be meaningfully compared. We introduce a Bayesian nonparametric hierarchical modeling approach for accomplishing both calibration and cell classification simultaneously in a unified probabilistic manner. Three important features of our method make it particularly effective for analyzing multi-sample cytometry data: a nonparametric mixture avoids prespecifying the number of cell clusters; a hierarchical skew normal kernel that allows flexibility in the shapes of the cell subsets and cross-sample variation in their locations; and finally the “coarsening” strategy makes inference robust to departures from the model not captured by the skew normal kernels. We demonstrate the merits of our approach in simulated examples and carry out a case study in the analysis of a multi-sample cytometry data set. We provide an R package for our method.

Contaminated Gibbs-Type Priors

Gibbs-type priors are combinatorial processes widely used as key components in several Bayesian nonparametric models. By virtue of their flexibility and mathematical tractability, they turn out to be predominant priors in species sampling problems and mixture modeling. We introduce a new family of processes which extends the Gibbs-type one, by including a contaminant component in the model to account for an excess of observations with frequency one. We first investigate the induced random partition, the associated predictive distribution, the asymptotic behavior of the total number of blocks and the number of blocks with a given frequency: all the results we obtain are in closed form and easily interpretable. A remarkable aspect of contaminated Gibbs-type priors relies on their predictive structure, compared to the one of the standard Gibbs-type family: it depends on the additional sampling information on the number of observations with frequency one out of the observed sample. As a noteworthy example we focus on the contaminated version of the Pitman-Yor process, which turns out to be analytically tractable and computationally feasible. Finally we pinpoint the advantage of our construction in different applications: we show how it helps to improve predictive inference in a species-related dataset exhibiting a high number of species with frequency one; we also discuss the use of the proposed construction in mixture models to perform density estimation and outlier detection.

MODELLING CRIME RATES IN INDONESIA USING TRUNCATED SPLINE ESTIMATOR

Criminal acts are actions that violate the law and can arise from various factors such as emotions, psychological pressure, and others. Crime rate is a number that indicate the level of crime vulnerability in a certain area at a certain time. Higher crime rates correspond to increased vulnerability in an area, and vice versa. Among various forms of criminal acts, the number of criminal acts and narcotics crimes in Indonesia tends to increase in 2020 and 2021. The aim of the research is to identify the characteristics of crime rate data based on the number of decency and narcotics incidents in Indonesia using a nonparametric regression approach. This research uses a nonparametric regression method spline truncated and linear regression as comparison. It was found that West Papua Province has the highest crime rate, based on a comparison between linear regression model and truncated spline nonparametric regression model, it can be concluded that the best model is the truncated spline nonparametric regression model with a Generalized Cross Validation (GCV) of 2468.487 and a coefficient of determination of 0 .7389091, indicating that approximately 73% of the variability of the dependent variable can be explained by the independent variables included in the model.

APPLICATION OF NONPARAMETRIC REGRESSION SPLINE TRUNCATED FOR MODELING THE HEIGHT OF YEOP CHAGI KICKS OF TAEKWONDO ATHLETES IN SAMARINDA CITY

Nonparametric regression is a model approach method that is used when the shape of the regression curve between the response variable and the predictor variable is assumed to have an unknown shape or pattern. One of the estimators in the nonparametric regression approach is the truncated spline which has the ability to handle data whose behavior changes at certain sub intervals. The purpose of this study was to obtain the estimated value of the parameters of the nonparametric regression model with a truncated spline approach at one knot point, two knot points, and three knot points for kick height data of yeop chagi taekwondo athletes in Samarinda City. The results showed that the truncated spline nonparametric regression model was the best in modeling high kick height data for yeop chagi taekwondo athletes in Samarinda City with three knot points. This model has the minimum Generalized Cross Validation (GCV) value of 7.94 with an R2 value of 94.72% and a Mean Square Error (MSE) value of 2.62. Based on the results of the model parameter significance test, it was concluded that the factors that influence the kick height of the yeop chagi taekwondo athlete in Samarinda City are flexibility, leg power, leg length, and waist circumference.

A nonparametric proportional risk model to assess a treatment effect in time-to-event data.

Time-to-event analysis often relies on prior parametric assumptions, or, if a semiparametric approach is chosen, Cox's model. This is inherently tied to the assumption of proportional hazards, with the analysis potentially invalidated if this assumption is not fulfilled. In addition, most interpretations focus on the hazard ratio, that is often misinterpreted as the relative risk (RR), the ratio of the cumulative distribution functions. In this paper, we introduce an alternative to current methodology for assessing a treatment effect in a two-group situation, not relying on the proportional hazards assumption but assuming proportional risks. Precisely, we propose a new nonparametric model to directly estimate the RR of two groups to experience an event under the assumption that the risk ratio is constant over time. In addition to this relative measure, our model allows for calculating the number needed to treat as an absolute measure, providing the possibility of an easy and holistic interpretation of the data. We demonstrate the validity of the approach by means of a simulation study and present an application to data from a large randomized controlled trial investigating the effect of dapagliflozin on all-causemortality.

Forecasting and Early Warning of Wind‐Farm‐Tripping Event under Strong‐Wind and Cold‐Wave Conditions Based on Combined Nonparametric Estimation Model and K‐Value Method

Wind power, as a pivotal renewable energy source, is increasingly vital in the global energy structure transformation. However, extreme weather, such as strong winds and cold waves, can result in rapid fluctuations in wind power generation and even lead to large‐scale wind‐farm‐tripping events. Consequently, forecasting and early warning of these events under extreme weather conditions are crucial for ensuring the safe operation of the power system. In this article, a forecasting and early warning method for wind‐farm‐tripping events is proposed, which is based on a multi‐nonparametric estimation approach. In this article, the error distribution of forecasting wind speed and temperature in numerical weather prediction (NWP) is modeled, and interval forecasting for these parameters is conducted. When forecasting intervals of NWP reach the thresholds of wind‐turbine‐tripping protection strategy, it will lead to the wind‐farm‐power‐tripping events. Furthermore, in this article, a method for calculating the wind‐power‐tripping capacity based on the K‐value method is introduced. The effectiveness of the proposed method is verified through actual wind‐power‐tripping events in the wind farm. The methodology proposed in the article provides a solution of early warning and forecasting for strong–wind‐ and cold‐wave wind‐farm‐tripping events.

Time to improvement of pain, morning stiffness, fatigue, and disease activity in patients with ankylosing spondylitis treated with tofacitinib: a post hoc analysis

BackgroundTofacitinib is an oral Janus kinase inhibitor for treatment of ankylosing spondylitis (AS). Time to improvement in core domains of AS was estimated in tofacitinib-treated patients with AS.MethodsThis post hoc analysis used phase 3 trial data from patients with AS receiving tofacitinib 5 mg twice daily or placebo to week (W)16; all patients received open-label tofacitinib W16–48. Outcomes: nocturnal pain; total back pain; fatigue, spinal pain, peripheral joint pain/swelling, enthesitis, and morning stiffness (Bath AS Disease Activity Index [BASDAI] questions 1–6); BASDAI total score; AS Disease Activity Score (ASDAS). Median time to improvement events was estimated using non-parametric Kaplan-Meier models. Improvement events were defined as initial (first post-baseline observation) and continued (sustained for 2 consecutive visits) ≥ 30% and ≥ 50% improvement in back/nocturnal pain or BASDAI questions/total scores, or ASDAS improvement ≥ 1.1 and ≥ 2.0 points.Results269 patients (tofacitinib: n = 133; placebo-to-tofacitinib: n = 136) were assessed. Median time to improvement was shorter, and more patients experienced improvements with tofacitinib vs. placebo-to-tofacitinib; differences observed from W2 (first post-baseline assessment). Median time to initial (continued) ≥ 30% pain improvement was 4 (4–8) weeks for tofacitinib vs. 24 (24) weeks for placebo-to-tofacitinib (8 [8] weeks post-switch). Median time to initial (continued) ≥ 50% improvement of pain, peripheral joint pain/swelling and enthesitis, morning stiffness, BASDAI total score, and fatigue was 8–24 (12–40) weeks with tofacitinib vs. 24–32 weeks (32 weeks–not estimable [NE]) with placebo-to-tofacitinib. Median time to initial (continued) ASDAS improvement ≥ 1.1 points was 4 (8) weeks for tofacitinib vs. 24 (24) weeks for placebo-to-tofacitinib, and NE for improvement ≥ 2.0 points with either treatment.ConclusionsImprovements in AS core domains occurred more rapidly with tofacitinib vs. placebo-to-tofacitinib. Half of tofacitinib-treated patients with AS will likely experience improvements ≥ 30% in pain and ≥ 1.1 points in ASDAS during month (M)1, ≥ 50% improvement in nocturnal pain and enthesitis by M2, and in morning stiffness by M3. Results show that initiating tofacitinib as soon as possible is associated with quicker improvements in AS core domains vs. delaying treatment.Trial registrationClinicalTrials.gov, NCT03502616, 11 April 2018.

#1635 In hemodialysis patients, COVID-19 vaccination mitigates the variability of clinical and laboratory variables

Abstract Background and Aims Despite prior studies on the clinical impact of COVID-19 and the documented efficacy of COVID-19 vaccinations in hemodialysis (HD) patients, the exact effects of COVID-19 vaccinations on various clinical biomarkers before and after the diagnosis of COVID-19 have not been established. In particular, the longitudinal trajectories of clinical and laboratory parameters remain unknown. We aim to investigate clinical and laboratory variables around the time of COVID-19 diagnosis and how vaccination impacts population trajectories. Method We studied 53,223 HD patients from Fresenius Kidney Care, a large U.S. dialysis organization, who received COVID-19 vaccination between January 1, 2021 and August 2, 2021. Depending on whether or not COVID-19 diagnosis happened after the first dose of vaccination for Johnson & Johnson or after the second dose for all other vaccines, we created two patient cohorts comprising patients infected before (N = 47,443) and after (N = 5780) full vaccination. A nonparametric cubic spline model for flexibility was fitted to the patient data averaged for each day between 60 days before and 60 days after the COVID-19 diagnosis. Clinical parameters include ones that are recorded at each HD treatment (pre-HD temperature, TEMP; pre-HD systolic blood pressure, SBP; pre-HD weight, WT; pre-HD pulse rate, PULSE; intradialytic weight gain, IDWG) and ones that are recorded less frequently (hemoglobin, HGB; white blood cell, WBC; neutrophil-lymphocyte ratio, NLR; platelet count, PLA; albumin, ALB). Results All longitudinal trajectory patterns undergo changes in the days before and after the diagnosis of COVID-19. In both the vaccinated and unvaccinated cohorts, there is an initial increase in TEMP, PULSE, and NLR up to 14 days preceding the diagnosis of COVID-19, followed by a subsequent decrease. Conversely, WT and IDWG exhibit a decline up to 14 days before the diagnosis of COVID-19 and an increase thereafter. ALB maintains a decreasing trend from 14 days prior until 20 days after before increasing again. In the unvaccinated cohort, SBP and WBC start decreasing up to 20 days ahead of diagnosis of COVID-19, whereas the unvaccinated cohort maintains a steady trend, increasing for SBP and decreasing for WBC. The lasting effects of COVID-19 can be found up to 60 days after diagnosis, e.g., WT, PULSE, and HGB. Variations in trend shapes are evident between the vaccinated and unvaccinated cohorts across all variables. Notably, all variables in the vaccinated cohort demonstrate smaller changes around the days of COVID-19 diagnosis. Conclusion We discovered trend changes in multiple variables in the days leading up to COVID-19 diagnosis, corroborating earlier findings. This finding lays the foundation for early detection ahead of regular COVID-19 tests. Prior COVID-19 vaccination mitigates the effect of COVID-19 on several important clinical and laboratory indicators, supporting the need for continued vaccination efforts. Lasting effects from infection are prevalent, extending beyond 60 days after confirmation. Further research is warranted to understand their long-term impact.

An Assessment of Container Seaport Efficiency Determinants

Maritime transport plays a pivotal role in the global economy, facilitating the majority of international trade and serving as a cornerstone for efficient and expansive logistics networks. The proliferation of economic globalisation has resulted in a significant upsurge in intercontinental transactions, thereby fostering the utilisation of ports and shipping enterprises as cost-effective and expeditious means of accessing a wide range of destinations in Europe, Asia, Africa, and North America. The objective of this study is to evaluate the significance of five exogenous variables, namely, GDP per capita, water depth, commodity-type diversification, management model, and European directional division, in relation to the performance of seaports. Measuring the impact of exogenous variables in seaport performance is crucial for understanding how external factors influence efficiency, enabling informed decision-making, and facilitating the development of targeted policies for sustainable and effective port operations. This assessment will be conducted using robust benchmarking analysis methods, specifically the nonparametric order-α model. Several findings suggest that there is a negative relationship between GDP per capita and the performance of seaports when GDP per capita reaches very high levels. However, seaports located in regions with lower GDP per capita tend to exhibit superior performance. The inefficiency of southern seaports is evident, whereas seaports located in Central/Eastern Europe exhibit superior performance, irrespective of their model orientation. These findings underscore the importance of considering economic context and regional factors in understanding seaport performance and highlight potential areas for improvement in southern seaports.

A nonparametric Bayesian item response modelling approach for clustering items and individuals simultaneously

Item response theory (IRT) is a popular modelling paradigm for measuring subject latent traits and item properties according to discrete responses in tests or questionnaires. There are very limited discussions on heterogeneity pattern detection for both items and individuals. In this paper, we introduce a nonparametric Bayesian approach for clustering items and individuals simultaneously under the Rasch model. Specifically, our proposed method is based on the mixture of finite mixtures (MFM) model, which obtains the number of clusters and the clustering configurations for both items and individuals simultaneously. The performance of parameter estimation and parameter clustering under the MFM Rasch model is evaluated by simulation studies, and two real examples are applied to further illustrate the MFM Rasch modelling.

Construction and optimization of non-parametric analysis model for meter coefficients via back propagation neural network

This study addresses the drawbacks of traditional methods used in meter coefficient analysis, which are low accuracy and long processing time. A new method based on non-parametric analysis using the Back Propagation (BP) neural network is proposed to overcome these limitations. The study explores the classification and pattern recognition capabilities of the BP neural network by analyzing its non-parametric model and optimization methods. For model construction, the study uses the United Kingdom Domestic Appliance-Level Electricity dataset’s meter readings and related data for training and testing the proposed model. The non-parametric analysis model is used for data pre-processing, feature extraction, and normalization to obtain the training and testing datasets. Experimental tests compare the proposed non-parametric analysis model based on the BP neural network with the traditional Least Squares Method (LSM). The results demonstrate that the proposed model significantly improves the accuracy indicators such as mean absolute error (MAE) and mean relative error (MRE) when compared with the LSM method. The proposed model achieves an MAE of 0.025 and an MRE of 1.32% in the testing dataset, while the LSM method has an MAE of 0.043 and an MRE of 2.56% in the same dataset. Therefore, the proposed non-parametric analysis model based on the BP neural network can achieve higher accuracy in meter coefficient analysis when compared with the traditional LSM method. This study provides a novel non-parametric analysis method with practical reference value for the electricity industry in energy metering and load forecasting.

Measurement of efficiency and its drivers in the Chilean banking industry.

This paper estimates efficiency measures for the banking system in Chile for the period 2000-2019. In contrast to previous studies, we use input-distance functions, introduce the nonparametric slack-based model, and choose the intermediate inputs approach in determining inputs and outputs. Our results suggest that the Chilean system has achieved relatively high levels of efficiency, although with no significant variation over the sample period. Ownership (government, foreign and public) and size had a positive impact on efficiency. On average, mergers and acquisitions seem to have targeted highly efficient banks in order to improve the overall efficiency of the controlling institution in the short run. Other sources of efficiency gains could be an increase in bond funding or a reduction in expenses and capital holdings. The latter could be induced by deepening the local derivatives market.

An assessment on the new impetus of green energy development and its impact on climate change: a non-linear perspective.

The purpose of this article is to investigate the new driving forces behind China's green energy and further assess the impact of green energy on climate change. The existing literature has used linear methods to investigate green energy, ignoring the non-linear relationships between economic variables. The nonparametric models can accurately simulate nonlinear relationships between economic variables. This paper constructs a nonparametric additive model and uses it to explore green energy. The empirical results show that the impact of green finance on green energy is more prominent in the later stage (a U-shaped impact). Fiscal decentralization also exerts a positive U-shaped impact, meaning that expanding local fiscal autonomy has contributed to green energy growth in the later stage. Similarly, the impact of oil prices and foreign direct investment demonstrates a positive U-shaped pattern. However, the nonlinear impact of environmental pressure displays an inverted U-shaped pattern. Furthermore, this article explores the impact of green energy on climate change and its impact mechanisms. The results exhibit green energy generates a positive U-shaped impact on climate change, meaning that the role of green energy in mitigating climate change gradually becomes prominent over time. Mechanism analysis exhibits that industrial structure and energy structure both produce a nonlinear influence on climate change.

Using Unmanned Aerial Vehicles and Multispectral Sensors to Model Forage Yield for Grasses of Semiarid Landscapes

Forage yield estimates provide relevant information to manage and quantify ecosystem services in grasslands. We fitted and validated prediction models of forage yield for several prominent grasses used in restoration projects in semiarid areas. We used field forage harvests from three different sites in Northern Utah and Southern California, USA, in conjunction with multispectral, high-resolution UAV imagery. Different model structures were tested with simple models using a unique predictor, the forage volumetric 3D space, and more complex models, where RGB, red edge, and near-infrared spectral bands and associated vegetation indices were used as predictors. We found that for most dense canopy grasses, using a simple linear model structure could explain most (R2 0.7) of the variability of the response variable. This was not the case for sparse canopy grasses, where a full multispectral dataset and a non-parametric model approach (random forest) were required to obtain a maximum R2 of 0.53. We developed transparent protocols to model forage yield where, in most circumstances, acceptable results could be obtained with affordable RGB sensors and UAV platforms. This is important as users can obtain rapid estimates with inexpensive sensors for most of the grasses included in this study.

Enabling efficient regional seismic fragility assessment of multi‐component bridge portfolios through Gaussian process regression and active learning

AbstractRegional seismic fragility assessment of bridge portfolios must address the embedded uncertainties and variations stemming from both the earthquake hazard and bridge attributes (e.g., geometry, material, design detail). To achieve bridge‐specific fragility assessment, multivariate probabilistic seismic demand models (PSDM) have recently been developed that use both the ground motion intensity measure and bridge parameters as inputs. However, explicitly utilizing bridge parameters as inputs requires numerous nonlinear response history analyses (NRHAs). In this situation, the associated computational cost increases exponentially for high‐fidelity bridge models with complex component connectivity and sophisticated material constitutive laws. Moreover, it remains unclear how many analyses are sufficient for the response data and the resulting demand model to cover the entire solution space without overfitting. To deal with these issues, this study integrates Gaussian process regression (GPR) and active learning (AL) into a multistep workflow to achieve efficient regional seismic fragility assessment of bridge portfolios. The GPR relaxes the probability distribution assumptions made in typical cloud analysis‐based PSDMs to enable heteroskedastic nonparametric seismic demand modeling. The AL leverages the varying standard deviation to select the least but most representative bridge‐model‐ground‐motion sample pairs to conduct NRHA with much‐improved efficiency. Both independent and correlated multi‐output GPRs are proposed to deal with bridge portfolios with seismic demand correlations among multiple components (column, bearing, shear key, abutment, unseating, and joint seal). Considering a single benchmark highway bridge class in California as the case study, the AL‐GPR framework and the associated component‐level fragility results are investigated in terms of their efficiency, accuracy, and robustness. The fragility results show that 70 AL‐selected samples would enable the GPR to derive bridge‐specific fragility models comparable to the ones using the multiple stripes analysis approach with 1950 ground motions considered for each individual bridge. The AL‐GPR model also successfully captures the physics of how bridge span length, deck area, column slenderness, and steel reinforcement ratio would change the damage state exceedance probabilities of different bridge components. The efficiency of AL stems from the fact that, with the multi‐output independent GPR, a stable and reliable fragility model can be achieved using 50 AL‐selected samples compared to at least 270 randomly chosen samples. The proposed methodology advances the state of the art in enabling more efficient and reliable regional seismic fragility assessment of multi‐component bridge portfolios.

Developing Parametric and Nonparametric Models for Model-Informed Precision Dosing: A Quality Improvement Effort in Vancomycin for Patients With Obesity.

Both parametric and nonparametric methods have been proposed to support model-informed precision dosing (MIPD). However, which approach leads to better models remains uncertain. Using open-source software, these 2 statistical approaches for model development were compared using the pharmacokinetics of vancomycin in a challenging subpopulation of class 3 obesity. Patients on vancomycin at the University of Vermont Medical Center from November 1, 2021, to February 14, 2023, were entered into the MIPD software. The inclusion criteria were body mass index (BMI) of at least 40 kg/m2 and 1 or more vancomycin levels. A parametric model was created using nlmixr2/NONMEM, and a nonparametric model was created using metrics. Then, a priori and a posteriori predictions were evaluated using the normalized root mean squared error (nRMSE) for precision and the mean percentage error (MPE) for bias. The parametric model was evaluated in a simulated MIPD context using an external validation dataset. In total, 83 patients were included in the model development, with a median age of 56.6 years (range: 24-89 years), and a median BMI of 46.3 kg/m2 (range: 40-70.3 kg/m2). Both parametric and nonparametric models were 2-compartmental, with creatinine clearance and fat-free mass as covariates to c clearance and volume parameters, respectively. The a priori MPE and nRMSE for the parametric versus nonparametric models were -6.3% versus 2.69% and 27.2% versus 30.7%, respectively. The a posteriori MPE and RMSE were 0.16% and 0.84%, and 13.8% and 13.1%. The parametric model matched or outperformed previously published models on an external validation dataset (n = 576 patients). Minimal differences were found in the model structure and predictive error between the parametric and nonparametric approaches for modeling vancomycin class 3 obesity. However, the parametric model outperformed several other models, suggesting that institution-specific models may improve pharmacokinetics management.

Testing for homogeneous treatment effects in linear and nonparametric instrumental variable models

. The hypothesis of homogeneous treatment effects is central to the instrumental variables literature. This assumption signifies that treatment effects are constant across all subjects. It allows to interpret instrumental variable estimates as average treatment effects over the whole population of the study. When this assumption does not hold, the bias of instrumental variable estimators can be greater than that of naive estimators ignoring endogeneity. This article develops two tests for the assumption of homogeneous treatment effects when the treatment is endogenous and an instrumental variable is available. The tests leverage a covariable that is (jointly with the error terms) independent of a coordinate of the instrument. This covariate does not need to be exogenous. The first test assumes that the potential outcomes are linear in the regressors and is computationally simple. The second test is nonparametric and relies on Tikhonov regularization. The treatment can be either discrete or continuous. We show that the tests have asymptotically correct level and asymptotic power equal to one against a range of alternatives. Simulations demonstrate that the proposed tests attain excellent finite sample performances. The methodology is also applied to the evaluation of returns to schooling and demand estimation in a fish market.

The impact of repayment obligations arising as a by‐product of input use on partial inefficiency: Evidence from Western Australian farm businesses

AbstractFarm businesses often use debt to finance the purchase inputs and meet operational costs. Selected studies have investigated the impact of debt use on farm performance as measured using technical efficiency. However, no prior studies in agriculture have considered treating the debt repayment obligation created as a by‐product of production when benchmarking farm performance. This study employs nonparametric directional distance function models to quantify the impact of debt repayment as a by‐product of input use purchased on credit on farm‐level partial inefficiency using a panel data of 54 mixed enterprise broadacre farms in Western Australia from 2002 to 2011. The study finds that omitting repayment obligations created in the production process in analysis results in underestimating partial inefficiency scores. Farm size, production diversification and rainfall zone are significant drivers of partial inefficiency. The implication of the study's findings is that failure to account for repayment obligations created in the production process may result in erroneous policy suggestions that undermine the efforts of farm businesses to minimise or avoid financial stress.