Smoothing Spline Regression Research Articles

66 A novel analytical method for identifying periods of increased mortality in individual wean-to-finish pig barns

Abstract Mortality rates in all phases of pork production have increased over the past several years. Decreasing mortality rate is of utmost economic importance to producers, especially in weaning-to-finishing pigs where additional investment in feed, medications, and other inputs are expended. A novel analytical method was used to identify sequences of days of increased mortality, defined as mortality episodes, within cohorts of weaning-to-finishing pigs. Cubic smoothing spline regression equations were estimated between days post placement (DPP) and mortality (total daily number of dead and euthanized pigs) for 42 groups of pigs placed in 12 rooms within 6 barns (2 rooms per barn) in Illinois and Iowa from December 2020 to July 2022 (n = 5,676 observation days). For each group, the instantaneous slope between DPP and mortality from spline regression was calculated and used to identify changepoints [i.e., peaks (negative instantaneous slope at DPPt and positive instantaneous slope at DPPt-1) and valleys (positive instantaneous slope at DPPt and negative instantaneous slope at DPPt-1)]. Within each mortality sequence, the maximum and minimum instantaneous slope were considered the start and end of the mortality episode, respectively. Mortality episodes were only considered high mortality episodes if the following metrics were satisfied: 1) greater than or equal to 1 mortality per day, or 2) greater than or equal to 1/3 of the days in the mortality sequence had at least 1 mortality. Days were classified as “start days” if they were within 1 d from the maximum instantaneous slope; likewise, we classified a day as “end days” if it was 1 d from the minimum instantaneous slope. Any day in between the start and end of a mortality episode were defined as “peak days”, and a “normal day” was all other days that did not fall within the start, peak, or end of a high mortality episode. Least-squares means for predicted mortality from a negative binomial generalized linear model showed that mortality was highest and lowest for peak days and normal days, respectively (3.475 ± 0.1729 and 0.591 ± 0.0156 pigs, respectively; P < 0.05; Table 1). Mortality during start or end days of an episode did not differ from each other (2.185 ± 0.1973 and 2.069 ± 0.1881 pigs, respectively; P > 0.05; Table 1) but were significantly greater and lower than a normal or peak day, respectively (P < 0.05; Table 1). The proposed analytical method provides more precise classification of mortality episodes in pig cohorts than previously proposed methods. Results from this method can be used in models to predict the start of mortality episodes, providing ample time for intervention to reduce the overall number of dead pigs in wean-to-finish pig production.

The Impact of the COVID-19 Epidemic on the U.S. Senior Housing Market—Analysis of Changes in Brookdale’s Market Capitalization Based on Smoothing Splines Regression

The U.S. population is aging, with a large portion of the population already entering retirement age. As a result, demand in the senior living industry continues to increase. This article explores the impact of the COVID-19 pandemic on the senior living industry, focusing on changes in market capitalization for industry leader Brookdale. Based on Brookdale's market cap data from 2005 to 2023, smoothing spline regression was used to analyze the impact of the pandemic on the company's market worth. Three different cross-validation methods including ordinary cross-validation (OCV), approximate cross-validation (ACV) and generalized approximate cross-validation (GACV) were tested for regression parameter selection. The study found that ACV provided the best model fit with an R-squared of 0.7181. The results show that Brookdale's market capitalization has experienced a significant decline during the COVID-19 outbreak, and the predicted values are consistent with the actual reduction in Brookdale's market capitalization. Factors contributing to the decline include lower occupancy rates in senior care facilities and the impact of the pandemic on the overall economy. This underscores the likelihood of challenges within the senior living industry during public health emergencies, warning both investors and operators to prepare for potential risks. The employment of the smoothing spline model in this study enhances the precision of market trend representation and facilitates rational decision-making among industry stakeholders.

Nonparametric Smoothing Spline Approach in Examining Investor Interest Factors

The nonparametric approach is an appropriate approach for patterns of relationships between predictor variables and response variables that are not or have not been known in form. In other words, there is no complete information about the pattern of relationships between variables. Curve estimation is determined based on relationship patterns in existing data. The nonparametric approach has great flexibility for estimating regression curves. This study aims to form a model on investor interest factors in improving tourism investment decisions with a nonparametric approach. The nonparametric method used is the smoothing spline regression method. The smoothing spline method is used because the modeling results from the smoothing spline approach can follow the relationship model between variables contained in the data. Thus, this method really helps researchers to model relationships between variables that are not linear and whose linear form is unknown. The results of the analysis showed that the nonparametric smoothing spline regression analysis method could model data by 94.63%, indicates that data variance can be explained by 94.63% with models, while other variance outside the study explain the remaining 5.37%. That is, investment motivation is one of the most important factors to improve investment decisions.

The Efficiency of the Proposed Smoothing Method over the Classical Cubic Smoothing Spline Regression Model with Autocorrelated Residual

Spline smoothing is a technique used to filter out noise in time series observations when predicting nonparametric regression models. Its performance depends on the choice of the smoothing parameter. Most of the existing smoothing methods applied to time series data tend to over fit in the presence of autocorrelated errors. This study aims to determine the optimum performance value, goodness of fit and model overfitting properties of the proposed Smoothing Method (PSM), Generalized Maximum Likelihood (GML), Generalized Cross-Validation (GCV), and Unbiased Risk (UBR) smoothing parameter selection methods. A Monte Carlo experiment of 1,000 trials was carried out at three different sample sizes (20, 60, and 100) and three levels of autocorrelation (0.2, 05, and 0.8). The four smoothing methods' performances were estimated and compared using the Predictive Mean Squared Error (PMSE) criterion. The findings of the study revealed that: for a time series observation with autocorrelated errors, provides the best-fit smoothing method for the model, the PSM does not over-fit data at all the autocorrelation levels considered ( the optimum value of the PSM was at the weighted value of 0.04 when there is autocorrelation in the error term, PSM performed better than the GCV, GML, and UBR smoothing methods were considered at all-time series sizes (T = 20, 60 and 100). For the real-life data employed in the study, PSM proved to be the most efficient among the GCV, GML, PSM, and UBR smoothing methods compared. The study concluded that the PSM method provides the best fit as a smoothing method, works well at autocorrelation levels (ρ=0.2, 0.5, and 0.8), and does not over fit time-series observations. The study recommended that the proposed smoothing is appropriate for time series observations with autocorrelation in the error term and econometrics real-life data. This study can be applied to; non – parametric regression, non – parametric forecasting, spatial, survival, and econometrics observations.

The Efficiency of the Proposed Smoothing Method over the Classical Cubic Smoothing Spline Regression Model with Autocorrelated Residual

Spline smoothing is a technique used to filter out noise in time series observations when predicting nonparametric regression models. Its performance depends on the choice of the smoothing parameter. Most of the existing smoothing methods applied to time series data tend to overfit in the presence of autocorrelated errors. This study aims to determine the optimum performance value, goodness of fit and model overfitting properties of the proposed Smoothing Method (PSM), Generalized Maximum Likelihood (GML), Generalized Cross-Validation (GCV), and Unbiased Risk (UBR) smoothing parameter selection methods. A Monte Carlo experiment of 1,000 trials was carried out at three different sample sizes (20, 60, and 100) and three levels of autocorrelation (0.2, 05, and 0.8). The four smoothing methods' performances were estimated and compared using the Predictive Mean Squared Error (PMSE) criterion. The findings of the study revealed that: for a time series observation with autocorrelated errors, provides the best-fit smoothing method for the model, the PSM does not over-fit data at all the autocorrelation levels considered ( the optimum value of the PSM was at the weighted value of 0.04 when there is autocorrelation in the error term, PSM performed better than the GCV, GML, and UBR smoothing methods were considered at all-time series sizes (T = 20, 60 and 100). For the real-life data employed in the study, PSM proved to be the most efficient among the GCV, GML, PSM, and UBR smoothing methods compared. The study concluded that the PSM method provides the best fit as a smoothing method, works well at autocorrelation levels (ρ=0.2, 0.5, and 0.8), and does not over fit time-series observations. The study recommended that the proposed smoothing is appropriate for time series observations with autocorrelation in the error term and econometrics real-life data. This study can be applied to; non – parametric regression, non – parametric forecasting, spatial, survival, and econometrics observations.

A Subsampling Method for Regression Problems Based on Minimum Energy Criterion

The extraordinary amounts of data generated nowadays pose heavy demands on computational resources and time, which hinders the implementation of various statistical methods. An efficient and popular strategy of downsizing data volumes and thus alleviating these challenges is subsampling. However, the existing methods either rely on specific assumptions for the underlying models or acquire partial information from the available data. For regression problems, we propose a novel approach, termed adaptive subsampling with the minimum energy criterion (ASMEC). The proposed method requires no explicit model assumptions and “smartly” incorporates information on covariates and responses. ASMEC subsamples possess two desirable properties: space-fillingness and spatial adaptiveness. We investigate the limiting distribution of ASMEC subsamples and their theoretical properties under the smoothing spline regression model. The effectiveness and robustness of the ASMEC approach are also supported by a variety of synthetic examples and two real-life examples.

Research on Structural Flexibility and Acceptance Model (SFAM) Reconstruction Based on Disruption Innovation in the Social Humanities and Education Sector

The research objectives are as follows: (1) Developing a flexible structural model of the relationship between variables. (2) Develop a structural model that is robust with the assumptions of normality and homoscedasticity. (3) Obtain estimator properties from the flexible and robust SFAM structural model. (4) Obtaining hypothesis testing of each relationship constructed from a flexible and robust SFAM structural model. This research is integrated with a flexible and robust model approach based on nonparametric smoothing spline (RNSS) regression analysis which can capture the form of relationships that depend on empirical data, and the robustness of the model based on the distribution assumption and the assumption of homoscedasticity error variance. There are at least three transformation methods, namely SRS, MSI, and RASCH, which will be used in the development of the Structural Flexibility and Acceptance Model (SFAM). The results obtained from the research progress report are obtaining the development of a flexible structural model of the form of the relationship between variables, obtaining the development of a robust structural model of the assumptions of normality and homoscedasticity, obtaining the estimator properties of the flexible and robust SFAM structural model, and obtaining hypothesis testing. of each relationship constructed from a flexible and robust SFAM structural model. The originality of the theory in this study is very visible in the discovery of a new model, namely SFAM which can accommodate several things, which are the weaknesses of several existing analysis tools such as reciprocal and recursive models, more than one endogenous variable, flexible and robust models, overcoming inadmissible solutions, reflective indicators, formative , and reflective/formative (on the second-order), metric and non-metric data, and simultaneous processing of the input score data (through transformation to scale).

MODELING OF OPEN UNEMPLOYMENT RATE AND PERCENTAGE OF POOR POPULATION WITH NONPARAMETRIC REGRESSION

Poverty is often a topic discussed and debated in various national and international forums. The problem of poverty does not seem to be finished being discussed every day. Many factors have contributed to the high poverty rate in Indonesia, and one of them is the high unemployment rate. This research is aimed at modeling of open unemployment rate and percentage of poor population with nonparametric regression. The methods used are Nadaraya Watson Estimator (NEW), Local Polynomial Estimator (LPE) and Smoothing Spline regression. In choosing the best model using the smallest Mean Square Error (MSE) value. The pattern of the relationship between unemployment and the percentage of poor population based on the scatter plot shows an unclear pattern and does not follow a parametric regression pattern. The results of the comparison of the MSE value, smoothing spline has the smaller value than NWE

Prediction of Comprehensive Water Cut in Periodic Waterflood Reservoir Based on Variable Weight Combination Model

The comprehensive water cut of periodic waterflood reservoirs is random and volatile, and it is a non-stationary time series. In order to solve the problems of poor prediction results of conventional methods or heavy workload and long time-consuming in reservoir numerical simulation, an EMD-LSTM neural network model and a smoothing spline regression (Smoothing Spline) variable weight combined prediction model are proposed. This method introduces empirical mode decomposition (EMD) to process non-stationary and non-linear data, and combines the advantages of machine learning and curve regression to improve the prediction accuracy of the model. The variable weight combination model is used to predict the water content of the periodic water injection reservoir. The results show that the prediction accuracy of the variable weight combination model is significantly higher than that of the single model. The variable weight combination model can reduce the prediction error and can effectively and accurately predict the periodic water injection oil. The comprehensive water content of the reservoir can guide the adjustment of water injection parameters for periodic water injection reservoirs.

UNSUPERVISED METHODOLOGY TO IN-SEASON MAPPING OF SUMMER CROPS IN URUGUAY WITH MODIS EVI’S TEMPORAL SERIES AND MACHINE LEARNING

Abstract. This paper presents a new methodology for mapping summer crops in Uruguay, during the season, based on time-series analysis of the EVI vegetation index derived from the MODIS sensor. Time-series were processed with the k-means unsupervised machine learning algorithm. For this algorithm, the ideal number of clusters was estimated using the elbow method. Once the clusters were obtained, for each one, the average phenological signature was adjusted using a nonlinear smoothing spline regression technique. Additionally, using the derivative analysis, the key points of the curve were estimated (minimum, maximum and inflection points). When analyzing the average signature of each cluster, those whose signature follows the seasonal pattern of an agricultural crop (similar to a Gaussian function) were selected to generate a binary map of crops/non-crops. The estimated crop area is 2,336,525 hectares, higher than the official statistics of 1,667,400 hectares for the 2014–15 season. This overestimation can be explained by the resolution of the MODIS pixel (250 meters), where each has a different degree of purity; and commission errors. The methodology was validated with 5,317 ground truth points, with a general accuracy of 95.8%, kappa index of 85.6, production and user accuracy of 85.1% and 91.3% for crops/non-crops.

Charting the development of emotion comprehension and abstraction from childhood to adulthood using observer-rated and linguistic measures.

This study examined two facets of emotion development: emotion word comprehension (knowing the meaning of emotion words such as "anger" or "excitement") and emotion concept abstraction (representing emotions in terms of internal psychological states that generalize across situations). Using a novel emotion vocabulary assessment, we captured how a cross-sectional sample of participants aged 4-25 (N = 196) defined 24 emotions. Smoothing spline regression models suggested that emotion comprehension followed an emergent shape: Knowledge of emotion words increased across childhood and plateaued around age 11. Human coders rated the abstractness of participants' responses, and these ratings also followed an emergent shape but plateaued significantly later than comprehension, around age 18. An automated linguistic analysis of abstractness supported coders' perceptions of increased abstractness across age. Finally, coders assessed the definitional strategies participants used to describe emotions. Young children tended to describe emotions using concrete strategies such as providing example situations that evoked those emotions or by referring to physiological markers of emotional experiences. Whereas use of these concrete strategies decreased with age, the tendency to use more abstract strategies such as providing general definitions that delineated the causes and characteristics of emotions or by providing synonyms of emotion words increased with age. Overall, this work (a) provides a tool for assessing definitions of emotion terms, (b) demonstrates that emotion concept abstraction increases across age, and (c) suggests that adolescence is a period in which emotion words are comprehended but their level of abstraction continues to mature. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Penerapan Generalized Cross Validation dalam Model Regresi Smoothing Spline pada Produksi Ubi Jalar di Jawa Tengah

<p>Sweet Potato is a useful plant as a source carbohydrates, proteins, and is used as an animal feed and ingredient industry. Based on data from the Badan Pusat Statistik (BPS), the production fluctuations of the sweet potato in Central Java from year to year are caused by many factor. The production of sweet potato and the factors that affected it if they are described into a pattern of relationships then they do not have a specific pattern and do not follow a particular distribution, such as harvest area, the allocation of subsidized urea fertilizer, and the allocation of subsidized organic fertilizer. Therefore, the production model of sweet potato could be applied into nonparametric regression model. The approach used for nonparametric regression in this study is smoothing spline regression. The method used in regression smoothing spline is generalized cross validation (GCV). The value of the smoothing parameter (λ) is chosen from the minimum GCV value. The results of the study show that the optimum λ value for the factors of harvest area, urea fertilizer and organic fertilizer are 5.57905e-14, 2.51426e-06, and 3.227217e-13 that they result a minimum GCV i.e 2.29272e-21, 1.38391e-16, and 3.46813e-24.</p><p> </p><p><strong>Keywords</strong>: Sweet potato; nonparametric; smoothing spline; generalized cross validation.</p>

Reconstruction of Structural Flexibility And Acceptance Model (SFAM) in Green Marketing Mix Strategy

This research reconstructs the Structural Flexibility and Acceptance Model (SFAM), as a concept / theory / proposition step on statistical modeling. Existing and widely used statistical modeling, such as SEM, PLS, GSCA and WarpPLS, have flaws and still lack some flaws. SFAM reconstruction is done by reducing existing weaknesses and by adding some new and often needed statistical concepts to statistic users. Structural Flexibility and Acceptance Model (SFAM) is a very intense method of data analysis, especially in the field of social and humanities education. Research in the field in general is to investigate the system, in which one of the properties of the system is complex. The simplification solution can be done through statistical modeling. The reconstruction of Structural Flexibility and Acceptance Model (SFAM) will also be equipped with the concept of path analysis with category data, so it can be used to analyze category data. It is also integrated with a flexible and robust modeling approach based on nonparametric smoothing spline regression analysis (RNSS) capable of capturing the form of relationships that depend on empirical data, and the uniformity of free assumption-based models of assumptions and the assumption of homosexuality of variance errors.

A model-averaging approach for smoothing spline regression

ABSTRACTThis article considers nonparametric regression problems and develops a model-averaging procedure for smoothing spline regression problems. Unlike most smoothing parameter selection studies determining an optimum smoothing parameter, our focus here is on the prediction accuracy for the true conditional mean of Y given a predictor X. Our method consists of two steps. The first step is to construct a class of smoothing spline regression models based on nonparametric bootstrap samples, each with an appropriate smoothing parameter. The second step is to average bootstrap smoothing spline estimates of different smoothness to form a final improved estimate. To minimize the prediction error, we estimate the model weights using a delete-one-out cross-validation procedure. A simulation study has been performed by using a program written in R. The simulation study provides a comparison of the most well known cross-validation (CV), generalized cross-validation (GCV), and the proposed method. This new method is straightforward to implement, and gives reliable performances in simulations.

Smoothing spline regression estimation based on real and artificial data

In this article we introduce a smoothing spline estimate for fixed design regression estimation based on real and artificial data, where the artificial data comes from previously undertaken similar experiments. The smoothing spline estimate gives different weights to the real and the artificial data. It is investigated under which conditions the rate of convergence of this estimate is better than the rate of convergence of the ordinary smoothing spline estimate applied to the real data only. The finite sample size performance of the estimate is analyzed using simulated data. The usefulness of the estimate is illustrated by applying it in the context of experimental fatigue tests.

CHANGES IN CRIMINAL OFFENDING AROUND THE TIME OF JOB ENTRY: A STUDY OF EMPLOYMENT AND DESISTANCE

Does employment promote desistance from crime? Most perspectives assume that individuals who become employed are less likely to offend than those who do not. The critical issue has to do with the timing of employment transitions in the criminal trajectory. The turning point hypothesis expects reductions in offending after job entries, whereas the maturation perspective assumes desistance to have occurred ahead of successful transitions to legitimate work. Focusing on a sample of recidivist males who became employed during 2001–2006 (N = 783), smoothing spline regression techniques were used to model changes in criminal offending around the point of entry to stable employment. Consistent with the maturation perspective, the results showed that most offenders had desisted prior to the employment transition and that becoming employed was not associated with further reductions in criminal behavior. Consistent with the turning point hypothesis, we identified a subset of offenders who became employed during an active phase of the criminal career and experienced substantial reductions in criminal offending thereafter. However, this trajectory describes less than 2 percent of the sample. The patterns observed in this research suggest that transition to employment is best viewed as a consequence rather than as a cause of criminal desistance.

Comparison of Different Interpolation Techniques For Modelling Temperatures In Middle Blacksea Region

Objective of this study is to determine the best method for modeling and mapping monthly and annual temperatures (minimum, maximum, mean) of northern Turkey by using geographical information systems (GIS). Temperature data of 72 different meteorological observation stations were used for modeling and mapping purposes. Inverse Distance Weighting (IDW), Thin-plate smoothing spline (TPS), Simple Kriging (SK), Cokriging (CK) and Linear Regression Model (LR) were used to analyze spatial distribution of temperature data. The correlation coefficients were used to compare the methods. The data was divided in two different groups for validation procedure. The first group of data from 58 stations was used for analysis and the second group of data from 14 stations was used for testing purposes. Correlation coefficients among the estimated and measured monthly mean temperatures varied between 0.80 and 0.95. Correlation coefficients for all months were found to be significant (P < 0.01). In general, LR yielded the best results for monthly mean temperatures. While TPS was identified as the best method for monthly minimum temperatures for most of the months, LR, IDW, and CK methods yielded best results for other months. All methods yielded unsatisfactory results for maximum temperatures, especially for summer months.

Understanding and modelling heterogeneity of human preferences for engineering design

In today's competitive market, it is essential for companies to provide products which not only achieve high performance, but also appeal to the tastes of consumers. Therefore, a key element of design is an understanding of consumer preferences for product features. In this work, the random-effects ordered logit model is proposed as the modelling framework to capture the impact of both product and human attributes on consumers’ ratings of qualitative system and sub-system attributes. To support the modelling, a series of methodologies are developed to both understand and model the influence of consumer heterogeneity upon product preferences. To illustrate the methodologies, a human appraisal experiment for understanding preferences for automobile occupant package design is analysed. An issue with analysing human appraisal experiments is that the effect of respondent heterogeneity must be understood to separate the influence of design factors from that of human factors. Hierarchical Bayes estimation and cluster analysis are used to gain an understanding of respondent rating styles, which are subsequently modelled explicitly in the ordered logit model. Smoothing spline regression is used to determine the functional form of the ordered logit model. The proposed ordered logit model is validated using a vehicle design case study.

Modified Cp Criterion for Optimizing Ridge and Smooth Parameters in the MGR Estimator for the Nonparametric GMANOVA Model

Longitudinal trends of observations can be estimated using the generalized multivariate analysis of variance (GMANOVA) model proposed by [10]. In the present paper, we consider estimating the trends nonparametrically using known basis functions. Then, as in nonparametric regression, an overfitting problem occurs. [13] showed that the GMANOVA model is equivalent to the varying coefficient model with non-longitudinal covariates. Hence, as in the case of the ordinary linear regression model, when the number of covariates becomes large, the estimator of the varying coefficient becomes unstable. In the present paper, we avoid the overfitting problem and the instability problem by applying the concept behind penalized smoothing spline regression and multivariate generalized ridge regression. In addition, we propose two criteria to optimize hyper parameters, namely, a smoothing parameter and ridge parameters. Finally, we compare the ordinary least square estimator and the new estimator.

Estimating the accuracy of (local) cross-validation via randomised GCV choices in kernel or smoothing spline regression

In nonparametric regression, it is generally crucial to select ‘nearly’ optimal smoothing parameters for which the underlying average squared error (Δ ), with given weights, is ‘nearly’ minimised. The cross-validation (CV) selector or the GCV selector are popular for this task, but it has been observed by many statisticians that these selectors may happen to be ‘not sufficiently’ accurate in some situations. So a practical matter of great importance is the development of reliable estimates of this accuracy. The purpose of this paper is to show that the simulation of the randomised GCV selector or a simple general variant using an ‘augmented-randomised-trace’, can provide useful inferences, like consistent estimates of the standard error in the CV selector or of the expected increase of Δ due to this error. Furthermore, this also provides a tool for constructing more parsimonious curve estimates having almost the same asymptotic justification as the CV estimate, namely with similar increase of Δ up to a given factor. Rigorous proofs are given in the context of one-dimensional kernel regression. Simulated examples, also in this context, illustrate the usefulness of the methodology even at moderate sample sizes. Some direct extensions (for multi-dimensional kernels, equispaced splines) of the theoretical results are outlined. We give heuristics which indicate that the general methodology proposed in this article should be useful in many curve-, surface- or image-estimation problems when using spline-like smoothers.