Parametric Regression Techniques Research Articles

S-wave log construction through semi-supervised regression clustering using machine learning: A case study of North Sea fields

Accurate prediction of S-wave velocity from well logs is essential for understanding subsurface geological formations and hydrocarbon reservoirs. Machine learning techniques, including clustering and regression, have emerged as effective methods for indirectly estimating S-wave logs and other rock properties. In this study, we employed clustering algorithms to identify similarities among well log datasets, encompassing depth, sonic, porosity, neutron, and apparent density, facilitating the discovery of correlations among various wells. These identified correlations served as a foundation for predicting S-wave values using a novel semi-supervised approach. Our approach combined clustering, specifically k-means clustering, with different types of regressors, including Least Squares Regression (LSR), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Our results demonstrate the superior performance of this integrated approach compared to traditional regression methods. We validated our methodology using various parametric and non-parametric regression techniques, showcasing its effectiveness not only on wells within the training region but also on wells outside the study area. We achieved a significant improvement in the R2 score metric, ranging from 2.22% to 6.51%, and a reduction in Mean Square Error (MSE) of at least 31% when compared to predictions made without clustering. This study underscores the potential of machine learning techniques for accurate prediction of S-wave velocity and other rock properties, thereby enhancing our comprehension of subsurface geology and hydrocarbon reservoirs.

Evaluation of Data Fitting Approaches for Speed/Flow Density Relationships

This paper presents guidance on data-fitting approaches in the context of pedestrian and evacuation dynamics research. In particular, it examines parametric and non-parametric regression techniques for analysing speed/flow density relationships. Parametric models assume predefined functional forms, while non-parametric models provide flexibility to capture complex relationships. This paper evaluates a range of traditional statistical approaches and machine-learning techniques. It emphasises the importance of weighting unbalanced datasets to enhance model accuracy. Practical applications are illustrated using traffic and pedestrian evacuation data. This paper is intended to stimulate discussion on best practices for developing, calibrating, and testing macroscopic and microscopic evacuation models. It does not prescribe a one-size-fits-all solution for evacuation data fitting approaches, but it provides an overview of existing methods and analyses their advantages and limitations.

Machine Learning Estimation of Maximum Vertical Velocity from Radar

Abstract The quantification of storm updrafts remains unavailable for operational forecasting despite their inherent importance to convection and its associated severe weather hazards. Updraft proxies, like overshooting top area from satellite images, have been linked to severe weather hazards but only relate to a limited portion of the total storm updraft. This study investigates if a machine learning model, namely, U-Nets, can skillfully retrieve maximum vertical velocity and its areal extent from three-dimensional gridded radar reflectivity alone. The machine learning model is trained using simulated radar reflectivity and vertical velocity from the National Severe Storm Laboratory’s convection permitting Warn-on-Forecast System (WoFS). A parametric regression technique using the sinh–arcsinh–normal distribution is adapted to run with U-Nets, allowing for both deterministic and probabilistic predictions of maximum vertical velocity. The best models after hyperparameter search provided less than 50% root mean squared error, a coefficient of determination greater than 0.65, and an intersection over union (IoU) of more than 0.45 on the independent test set composed of WoFS data. Beyond the WoFS analysis, a case study was conducted using real radar data and corresponding dual-Doppler analyses of vertical velocity within a supercell. The U-Net consistently underestimates the dual-Doppler updraft speed estimates by 50%. Meanwhile, the area of the 5 and 10 m s−1 updraft cores shows an IoU of 0.25. While the above statistics are not exceptional, the machine learning model enables quick distillation of 3D radar data that is related to the maximum vertical velocity, which could be useful in assessing a storm’s severe potential. Significance Statement All convective storm hazards (tornadoes, hail, heavy rain, straight line winds) can be related to a storm’s updraft. Yet, there is no direct measurement of updraft speed or area available for forecasters to make their warning decisions from. This paper addresses the lack of observational data by providing a machine learning solution that skillfully estimates the maximum updraft speed within storms from only the radar reflectivity 3D structure. After further vetting the machine learning solutions on additional real-world examples, the estimated storm updrafts will hopefully provide forecasters with an added tool to help diagnose a storm’s hazard potential more accurately.

Principled Machine Learning

We introduce the underlying concepts which give rise to some of the commonly used machine learning methods, excluding deep-learning machines and neural networks. We point to their advantages, limitations and potential use in various areas of photonics. The main methods covered include parametric and non-parametric regression and classification techniques, kernel-based methods and support vector machines, decision trees, probabilistic models, Bayesian graphs, mixture models, Gaussian processes, message passing methods and visual informatics.

Does auditor ratification matter to bondholders? Evidence from new bond issues

We investigate the relation between shareholder votes on auditor ratification and the client’s credit risk, using both parametric and nonparametric regression techniques. Using data from 2006 to 2016, we show that the proportion of shareholder disapproval for auditors heterogeneously impacts credit risk, as shown by bond spread and bond rating. The results suggest that higher shareholder disapproval of the auditor has an adverse effect on yield spread and bond ratings after controlling for firm characteristics associated with audit quality, such as auditor size and tenure.

Intersections of machine learning and epidemiological methods for health services research.

The field of health services research is broad and seeks to answer questions about the health care system. It is inherently interdisciplinary, and epidemiologists have made crucial contributions. Parametric regression techniques remain standard practice in health services research with machine learning techniques currently having low penetrance in comparison. However, studies in several prominent areas, including health care spending, outcomes and quality, have begun deploying machine learning tools for these applications. Nevertheless, major advances in epidemiological methods are also as yet underleveraged in health services research. This article summarizes the current state of machine learning in key areas of health services research, and discusses important future directions at the intersection of machine learning and epidemiological methods for health services research.

Developing automated valuation models for estimating property values: a comparison of global and locally weighted approaches

Automated valuation models are widely used in real estate to provide estimates for property prices. Such models are typically developed through regression approaches. This study presents a comparative analysis about the performance of parametric and non-parametric regression techniques for developing reliable automated valuation models for residential properties. Different approaches are explored to incorporate spatial effects into the valuation process, covering both global and locally weighted models. The analysis is based on a large sample of properties from Greece during the period 2012–2016. The results demonstrate that linear regression models developed with a weighted spatial (local) scheme provide the best results, outperforming machine learning approaches and models that do not consider spatial effects.

Application of independent component analysis in regional flood frequency analysis: Comparison between quantile regression and parameter regression techniques

This paper examines the applicability of independent component analysis (ICA) in regional flood frequency analysis (RFFA). Data from 88 catchments in New South Wales, Australia are adopted in this study. The ICA is integrated with both quantile regression technique (QRT) and parameter regression technique (PRT). A total of eight climatic and catchment characteristics is used as potential predictor variables in the study. For ICA, independent components (IC) are used as the predictor variables and a ‘cumulative percent relevance’ criterion is adopted to select the best predictors. A leave-one-out validation is adopted to assess the performances of the competing models using a suit of statistical evaluation measures. It is found that the QRT model with four catchment characteristics, and the PR model with all the ICs as predictors outperform the other candidate models. These models provide a absolute median relative error in the range of 33% to 44% for the QRT model (with catchment characteristics as predictors), and 48% to 59% for the PRT model (with the ICs as predictors) for design flood estimation. This is comparable to the relative error values (57% to 64%) reported for the RFFA technique recommended in the Australian Rainfall and Runoff.

Long-Run Effects of Health Shocks in a Highly Regulated Labour Market

Based on administrative data covering employment, social security and hospital record histories, we investigate the effect of acute cardiovascular health shocks resulting in unplanned hospitalisation, on blue collars’ long-term labour outcomes in Italy. The Italian institutional setting, characterised by a highly regulated labour market and high job protection, is different from that of countries – mainly Nordic and Anglo-Saxon – covered in previous studies. We apply matching and parametric regression techniques to remove possible bias arising from observable and time-invariant unobservable confounders. Results point at sizeable and persistent reductions in employment and labour income, while hours and wage adjustments appear limited. Whereas a relatively generous social insurance system might compensate the earnings loss, our findings question the appropriateness of existing labour inclusion policies.

Missing observation approximation for spatio-temporal profile reconstruction in participatory sensor networks

Purpose Participatory wireless sensor networks (PWSN) is an emerging paradigm that leverages existing sensing and communication infrastructures for the sensing task. Various environmental phenomenon – P monitoring applications dealing with noise pollution, road traffic, requiring spatio-temporal data samples of P (to capture its variations and its profile construction) in the region of interest – can be enabled using PWSN. Because of irregular distribution and uncontrollable mobility of people (with mobile phones), and their willingness to participate, complete spatio-temporal (CST) coverage of P may not be ensured. Therefore, unobserved data values must be estimated for CST profile construction of P and presented in this paper. Design/methodology/approach In this paper, the estimation of these missing data samples both in spatial and temporal dimension is being discussed, and the paper shows that non-parametric technique – Kernel Regression – provides better estimation compared to parametric regression techniques in PWSN context for spatial estimation. Furthermore, the preliminary results for estimation in temporal dimension have been provided. The deterministic and stochastic approaches toward estimation in the context of PWSN have also been discussed. Findings For the task of spatial profile reconstruction, it is shown that non-parametric estimation technique (kernel regression) gives a better estimation of the unobserved data points. In case of temporal estimation, few preliminary techniques have been studied and have shown that further investigations are required to find out best estimation technique(s) which may approximate the missing observations (temporally) with considerably less error. Originality/value This study addresses the environmental informatics issues related to deterministic and stochastic approaches using PWSN.

Analysis of parametric and non-parametric regression techniques to model the wind turbine power curve

Wind turbine power curve provides technical specification of the wind turbine in the form of nominal wind power readings. This information may used to monitor the performance of the power system, estimate the power produced by the turbine, optimize the operational cost, and improve the reliability of the power system. However, this information is not sufficient to accomplish these tasks. To accomplish these tasks, the accurate modeling of the wind power curve is required. In this article, various curve fitting techniques, namely polynomial regression, locally weighted polynomial regression, spline regression, piecewise polynomial regression, and smoothing spline, have been applied to model the power curve of wind turbine. All these techniques have been used to model the power curve on National Renewable Energy Laboratory (NREL) 2012 dataset with site-id 124693.

Informal waste recycling activities: Implications for livelihood and health

Informal waste recycling activities are fast becoming commonplace in most developing countries. Using South Africa as a case study, this study characterizes informal collection of recyclable waste materials. It also explores the roles of informal collection of recyclable waste materials on the livelihood and health status of those who are involved in the activities. The study uses population-weighted General Household Surveys (GHS) covering years 2005–2014. Both parametric and non-parametric regression techniques were applied. For the most part, the results show upward trends in the proportions of those involved in collection of recyclable waste materials at the household level, and those earning a living from informal collection of recyclable waste. However, those who collect recyclable waste for a living are 3% more likely to suffer ill-health than those who do not. Hence, there is need for pragmatic actions to improve the working conditions of those involved by integrating innovative measures into the informal waste recycling system.

Statistical inference under imputation for proportional hazard model with missing covariates

ABSTRACTMissing covariate data are common in biomedical studies. In this article, by using the non parametric kernel regression technique, a new imputation approach is developed for the Cox-proportional hazard regression model with missing covariates. This method achieves the same efficiency as the fully augmented weighted estimators (Qi et al. 2005. Journal of the American Statistical Association, 100:1250) and has a simpler form. The asymptotic properties of the proposed estimator are derived and analyzed. The comparisons between the proposed imputation method and several other existing methods are conducted via a number of simulation studies and a mouse leukemia data.

Regional flood frequency analysis using spatial proximity and basin characteristics: Quantile regression vs. parameter regression technique

Despite wide use of regression-based regional flood frequency analysis (RFFA) methods, the majority are based on either ordinary least squares (OLS) or generalized least squares (GLS). This paper proposes ‘spatial proximity’ based RFFA methods using the spatial lagged model (SLM) and spatial error model (SEM). The proposed methods are represented by two frameworks: the quantile regression technique (QRT) and parameter regression technique (PRT). The QRT develops prediction equations for flooding quantiles in average recurrence intervals (ARIs) of 2, 5, 10, 20, and 100 years whereas the PRT provides prediction of three parameters for the selected distribution. The proposed methods are tested using data incorporating 30 basin characteristics from 237 basins in Northeastern United States. Results show that generalized extreme value (GEV) distribution properly represents flood frequencies in the study gages. Also, basin area, stream network, and precipitation seasonality are found to be the most effective explanatory variables in prediction modeling by the QRT and PRT. ‘Spatial proximity’ based RFFA methods provide reliable flood quantile estimates compared to simpler methods. Compared to the QRT, the PRT may be recommended due to its accuracy and computational simplicity. The results presented in this paper may serve as one possible guidepost for hydrologists interested in flood analysis at ungaged sites.

Regional flood frequency analysis method for Tasmania, Australia: a case study on the comparison of fixed region and region-of-influence approaches

A new regional flood frequency analysis method for Tasmania is developed using data from 53 gauged catchments. The Bayesian generalized least squares regression (BGLSR) approach is used to developing prediction equations for selected flood quantiles using a quantile regression technique (QRT) and the first three moments of the log Pearson Type 3 distribution by means of a parameter regression technique (PRT). Regions are formed in three ways: (a) a fixed-region approach is examined in which all the sites in Tasmania are assumed to form one region; (b) a region of influence (ROI) approach is investigated in which a region is formed around each of the sites based on the criterion of minimum model error variance; and (c) the state of Tasmania is divided into two fixed regions (east Tasmania and west Tasmania) based on their distinct rainfall regimes. Independent testing showed that the BGLSR-ROI approach can deal quite well with the uncertainty in the regional estimation, as shown by the various regression diagnostics. Furthermore, the PRT-ROI approach was found to provide quantile estimates that are generally more accurate and consistent than the QRT. Generally, the ROI approach outperformed the fixed-region approach for Tasmania.

Returns to Physical Capital in Ethiopia: Comparative Analysis of Formal and Informal Firms

Summary This paper investigates returns to capital in the formal and informal sectors in Ethiopia using parametric and semi-parametric regression techniques. Results show that there is a higher annual median return to capital in the informal sector (52–140%) than the formal sector (15–21%). However, informal firms might benefit from working formally as their capital stock gets larger. Marginal returns in the informal sector increase with: firm size, access to reliable markets, market uncertainty, and owners’ labor supply. On the other hand, marginal returns to capital and overall profitability decrease with capital stock, contrary to the poverty trap hypothesis.

PRM75 - Use Of Model Averaging Techniques in Cost-Effectiveness Analysis in Oncology

Often in cost-effectiveness analysis (CEA) of oncologic drugs, survival data from a randomized controlled trial are extrapolated to a lifetime horizon using parametric regression techniques. To capture parameter uncertainty in the analysis, regression parameters along with other model parameters are varied in probabilistic sensitivity analysis. However, structural uncertainty in the choice of regression model is rarely investigated. This study discusses the use of model averaging and provides an example to address structural uncertainty in CEA. Using a cohort partition model, the numbers of patients in “progression-free”, “progressed”, and “dead” health states were calculated directly from progression-free survival (PFS) and overall survival (OS) curves. Weibull, exponential, lognormal, log-logistic, generalized gamma, and Gompertz parametric models were used to extrapolated these curves to a lifetime horizon. Total costs, life year (LY), and quality adjusted life year (QALY) for each regression model were estimated. Weighted results across all models were calculated, based on weights that were derived from Akaike’s or Bayesian Information Criterion (AIC or BIC) parameters. Evaluating solely on BIC values, the lognormal distribution was identified as the best model for both survival curves. This resulted in the lowest observed ICERs. When model selection was based on considerations involving the log-cumulative hazard plots, clinical plausibility, and AIC/ BIC for each distribution, the Weibull distribution was selected for both curves, resulting in a 29% and 27% increase in the ICER for QALY and LY, respectively. Similar increases were observed when model averaging was applied using BIC-derived weights. In this case, model averaging produced results that were similar to those where model selection was based on multiple criteria. Choice of parametric models often has the biggest impact on the outcomes in CEAs in oncology. Model averaging takes into account the structural uncertainty surrounding the choice of parametric models.

Effects of soil nutrients and environmental factors on site productivity in Castanea sativa Mill. coppice stands in NW Spain

Ecological behaviour and productive capacity of chestnut (Castanea sativa Mill.) coppice stands are key factors in predicting forest growth and subsequent management decision, especially in areas where timber production is the primary objective. The effects of soil nutrients and environmental factors on site productivity in chestnut coppice stands in North-West Spain were studied. Site productivity described by site index was related to environmental characteristics, including edaphic, physiographic and climatic variables. The key factors affecting site productivity were evaluated according to two different statistical analyses: the CHAID procedure and parametric regression techniques. The CHAID algorithm applied separately to each type of variable revealed that the most important to explain SI were edaphic (sand and clay percentage, pH, stoniness) and climatic variables (summer and spring precipitation and mean annual temperature) (24 and 47 %, respectively). According to the regression tree and the parametric regression model for all variables, summer precipitation was the most significant variable (51 and 53 %, respectively). The results show the importance of climatic variables for chestnut coppice stands growth and provide further information about the ecology of the species in North-West Spain. The use of specimens from sites representing a wide range of habitats/growing conditions of this species means that both the results and methodology described here are of great relevance for improving the management of this species throughout its European range.

Semiparametric inference for the recurrent events process by means of a single-index model

In this paper, we introduce new parametric and semiparametric regression techniques for a recurrent event process subject to random right censoring. We develop models for the cumulative mean function and provide asymptotically normal estimators. Our semiparametric model which relies on a single-index assumption can be seen as a dimension reduction technique that, contrary to a fully nonparametric approach, is not stroke by the curse of dimensionality when the number of covariates is high. We discuss data-driven techniques to choose the parameters involved in the estimation procedures and provide a simulation study to support our theoretical results.

Derivation of short-duration design rainfalls using daily rainfall statistics

Design rainfall intensity–frequency–duration data are a basic input to many water-related development projects. To derive design rainfalls, one needs long period of recorded rainfall data. Although daily rainfall data are generally widely available, short-duration rainfall data are scarce. For many urban applications, design rainfalls for much shorter durations are needed, which cannot be obtained directly from daily read rainfall data. This paper presents a simple approach that can be adopted to derive design rainfalls of short durations using daily rainfall data and other physio-climatic characteristics using a novel ‘index frequency combined with parameter regression technique’. This uses L moments to reduce the impacts of sampling variability in the analysis. Furthermore, this adopts generalised least squares regression to account for the inter-station correlation of the rainfall data in the analysis. The proposed method is applied to a pilot data set consisting of 203 rainfall stations across Australia. An independent Monte Carlo cross-validation test shows that the proposed method is capable of generating consistent and accurate design rainfall estimates from 6-min to 12-h duration. The developed technique can be adapted to other countries where there is a scarcity of short-duration rainfall data, but daily rainfall data are abundant.