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Journal articles on the topic 'Threshold Regression Models'
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Hidalgo, Javier, Jungyoon Lee, and Myung Hwan Seo. "Robust inference for threshold regression models." Journal of Econometrics 210, no. 2 (2019): 291–309. http://dx.doi.org/10.1016/j.jeconom.2019.01.008.
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Lee, Sokbae, Myung Hwan Seo, and Youngki Shin. "Testing for Threshold Effects in Regression Models." Journal of the American Statistical Association 106, no. 493 (2011): 220–31. http://dx.doi.org/10.1198/jasa.2011.tm09800.
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Wong, Man-Yu, and Shuanglin Zhang. "Wavelet threshold estimation for additive regression models." Annals of Statistics 31, no. 1 (2003): 152–73. http://dx.doi.org/10.1214/aos/1046294460.
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Fong, Youyi, Chongzhi Di, Ying Huang, and Peter B. Gilbert. "Model-robust inference for continuous threshold regression models." Biometrics 73, no. 2 (2016): 452–62. http://dx.doi.org/10.1111/biom.12623.
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Greb, Friederike, Tatyana Krivobokova, Axel Munk, and Stephan von Cramon-Taubadel. "Regularized Bayesian Estimation of Generalized Threshold Regression Models." Bayesian Analysis 9, no. 1 (2014): 171–96. http://dx.doi.org/10.1214/13-ba850.
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Zhang, Tianyi. "Statistical Estimation in Piecewise Linear Regression Models." Asian Research Journal of Mathematics 21, no. 4 (2025): 39–45. https://doi.org/10.9734/arjom/2025/v21i4909.
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The kink regression model assumes that linear regression forms are separately modelled on two sides of an unknown threshold but still continuous at the threshold. This paper considers statistical estimation for piecewise linear regression models which are widely used in various fields to capture nonlinear relationships between variables. The estimators for the kink locations and regression coefficients are obtained by using the least squares method, a detailed explanation of the estimation process is provided. Furthermore, the proposed methodology is validated through an illustrative example using Monte Carlo random simulation, demonstrating its effectiveness in accurately capturing nonlinear patterns and changes in the data.
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Ngoc Son, Nguyen. "Public debt management and economic growth: A threshold regression approach." Public and Municipal Finance 12, no. 1 (2023): 62–72. http://dx.doi.org/10.21511/pmf.12(1).2023.06.
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This study deals with the impact of national debt on gross domestic product growth, which plays an essential role in economic development when the debt-to-GDP ratio achieves the optimal public debt ratio. The goal of this study is to comprehend the relationship between government debt and GDP growth, which becomes increasingly essential for economic development as the debt-to-GDP ratio approaches the optimal threshold of public debt. The study applied regression threshold models, unit roots, and Pearson correlation tests to the data collected in Vietnam from 2000 to 2020 to determine the optimum national debt-to-GDP threshold. The results show that the correlation between national debt-to-GDP and GDP growth was 85.2%. All the variables are stationary at the first difference and lag after one year, and the 38% threshold is the best level of national debt for GDP growth. This study contributes to the theoretical enhancement of the current knowledge of the factors that offer the Vietnamese government a point of reference for policy recommendations to control national debt successfully.
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Dimitriadou, Lida, Panagiotis Nastos, Kostas Eleftheratos, John Kapsomenakis, and Christos Zerefos. "Mortality Related to Air Temperature in European Cities, Based on Threshold Regression Models." International Journal of Environmental Research and Public Health 19, no. 7 (2022): 4017. http://dx.doi.org/10.3390/ijerph19074017.
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There is a wealth of scientific literature that scrutinizes the relationship between mortality and temperature. The aim of this paper is to identify the nexus between temperature and three different causes of mortality (i.e., cardiological, respiratory, and cardiorespiratory) for three countries (Scotland, Spain, and Greece) and eleven cities (i.e., Glasgow, Edinburgh, Aberdeen, Dundee, Madrid, Barcelona, Valencia, Seville, Zaragoza, Attica, and Thessaloniki), emphasizing the differences among these cities and comparing them to gain a deeper understanding of the relationship. To quantify the association between temperature and mortality, temperature thresholds are defined for each city using a robust statistical analysis, namely threshold regression analysis. In a more detailed perspective, the threshold used is called Minimum Mortality Temperature (MMT), the temperature above or below which mortality is at minimum risk. Afterward, these thresholds are compared based on the geographical coordinates of each city. Our findings show that concerning all-causes of mortality under examination, the cities with higher latitude have lower temperature thresholds compared to the cities with lower latitude. The inclusion of the relationship between mortality and temperature in the array of upcoming climate change implications is critical since future climatic scenarios show an overall increase in the ambient temperature.
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Seo, Myung Hwan, and Oliver Linton. "A smoothed least squares estimator for threshold regression models." Journal of Econometrics 141, no. 2 (2007): 704–35. http://dx.doi.org/10.1016/j.jeconom.2006.11.002.
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FEINAUER, ERIKA, KENDRA M. HALL-KENYON, and KIMBERLEE C. EVERSON. "Rethinking the Linguistic Threshold Hypothesis: Modeling the Linguistic Threshold among young Spanish–English Bilinguals." Bilingualism: Language and Cognition 20, no. 5 (2016): 886–902. http://dx.doi.org/10.1017/s1366728916000626.
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This study uses a discontinuous-linear regression methodological approach to test the Linguistic Threshold Hypothesis (LTH). Specifically, we investigate the following hypotheses: (1) the rate of transfer of literacy skills from L1 to L2 is a function of L2 oral language ability, (2) the rate of transfer from L1 to L2 accelerates when students cross a specified threshold(s) of L2 language oral ability, and (3) discontinuous change-point regression models fit the data better than linear regression interaction models. Across literacy skills, discontinuous change-point regression models revealed levels of L2 oral language at which transfer from L1 to L2 literacy was maximized, suggesting that the relationship between L2 language and cross-linguistic transfer is not constant for the young Spanish–English bilinguals in our study. Further, discontinuous change-point regression models fit the data better than linear interaction models, suggesting the importance of using models that better match the theoretical assumptions underpinning the LTH.
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Gørgens, Tue, and Allan H. Würtz. "Threshold Regression with Endogeneity for Short Panels." Econometrics 7, no. 2 (2019): 23. http://dx.doi.org/10.3390/econometrics7020023.
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This paper considers the estimation of dynamic threshold regression models with fixed effects using short panel data. We examine a two-step method, where the threshold parameter is estimated nonparametrically at the N-rate and the remaining parameters are estimated by GMM at the N -rate. We provide simulation results that illustrate advantages of the new method in comparison with pure GMM estimation. The simulations also highlight the importance of the choice of instruments in GMM estimation.
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Rhodes, Nathaniel J., J. Nicholas O'Donnell, Bryan D. Lizza, Milena M. McLaughlin, John S. Esterly, and Marc H. Scheetz. "Tree-Based Models for Predicting Mortality in Gram-Negative Bacteremia: Avoid Putting the CART before the Horse." Antimicrobial Agents and Chemotherapy 60, no. 2 (2015): 838–44. http://dx.doi.org/10.1128/aac.01564-15.
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ABSTRACTIncreasingly, infectious disease studies employ tree-based approaches, e.g., classification and regression tree modeling, to identify clinical thresholds. We present tree-based-model-derived thresholds along with their measures of uncertainty. We explored individual and pooled clinical cohorts of bacteremic patients to identify modified acute physiology and chronic health evaluation (II) (m-APACHE-II) score mortality thresholds using a tree-based approach. Predictive performance measures for each candidate threshold were calculated. Candidate thresholds were examined according to binary logistic regression probabilities of the primary outcome, correct classification predictive matrices, and receiver operating characteristic curves. Three individual cohorts comprising a total of 235 patients were studied. Within the pooled cohort, the mean (± standard deviation) m-APACHE-II score was 13.6 ± 5.3, with an in-hospital mortality of 16.6%. The probability of death was greater at higher m-APACHE II scores in only one of three cohorts (odds ratio for cohort 1 [OR1] = 1.15, 95% confidence interval [CI] = 0.99 to 1.34; OR2= 1.04, 95% CI = 0.94 to 1.16; OR3= 1.18, 95% CI = 1.02 to 1.38) and was greater at higher scores within the pooled cohort (OR4= 1.11, 95% CI = 1.04 to 1.19). In contrast, tree-based models overcame power constraints and identified m-APACHE-II thresholds for mortality in two of three cohorts (P= 0.02, 0.1, and 0.008) and the pooled cohort (P= 0.001). Predictive performance at each threshold was highly variable among cohorts. The selection of any one predictive threshold value resulted in fixed sensitivity and specificity. Tree-based models increased power and identified threshold values from continuous predictor variables; however, sample size and data distributions influenced the identified thresholds. The provision of predictive matrices or graphical displays of predicted probabilities within infectious disease studies can improve the interpretation of tree-based model-derived thresholds.
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Clapham, W. M., and J. M. Fedders. "Modeling vegetative development of berseem clover (Trifolium alexandrinum L.) as a function of growing degree days using linear regression and neural networks." Canadian Journal of Plant Science 84, no. 2 (2004): 511–17. http://dx.doi.org/10.4141/p02-143.
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Accurate models of berseem clover (Trifolium alexandrinum L.) development in relation to growing degree-days (GDD) would be useful to both producers and researchers. Predictive ability of linear regression models of plant development may be limited by choice of threshold temperature and the non-linear nature of plant development. Neural networks provide a robust approach to dealing with non-linearity, and may therefore be useful for modeling plant development. In exp.1, a numerical scale of plant development was created and used to describe growth of four cultivars of berseem clover (Bigbee, Joe Burton, Saidi and Tabor) under controlled environmental conditions (constant temperature of 12, 18 or 24°C per 12-h photoperiod) for up to 18 wk of vegetative growth. Simple linear regression and neural networks were used to model plant development in relation to GDD using a range of threshold temperatures. Predictive ability of the models was compared with the results from a second controlled environment study (exp. 2). The r2 of the linear and neural models produced in exp. 1 were maximized at GDD threshold temperatures of 0 to 2°C. Results from exp. 2 indicated that the predictive ability of neural models matched or exceeded that of the linear models for all threshold temperatures evaluated. Results of the current study suggests that neural network models are relatively insensitive to base temperatures across the range tested and may therefore be preferable when a priori knowledge of temperature thresholds is not available. Key words: Berseem clover, plant development, phenology modeling, growing degree days, base temperature, neural network modeling
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Duchesne, Thierry, and Jeffrey S. Rosenthal. "On the collapsibility of lifetime regression models." Advances in Applied Probability 35, no. 3 (2003): 755–72. http://dx.doi.org/10.1239/aap/1059486827.
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In this paper we derive conditions on the internal wear process under which the resulting time to failure model will be of the simple collapsible form when the usage accumulation history is available. We suppose that failure occurs when internal wear crosses a certain threshold or a traumatic event causes the item to fail. We model the infinitesimal increment in internal wear as a function of time, accumulated internal wear, and usage history, and we derive conditions on this function to get a collapsible model for the distribution of time to failure given the usage history. We reach the conclusion that collapsible models form the subset of accelerated failure time models with time-varying covariates for which the time transformation function satisfies certain simple properties.
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Duchesne, Thierry, and Jeffrey S. Rosenthal. "On the collapsibility of lifetime regression models." Advances in Applied Probability 35, no. 03 (2003): 755–72. http://dx.doi.org/10.1017/s0001867800012520.
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In this paper we derive conditions on the internal wear process under which the resulting time to failure model will be of the simple collapsible form when the usage accumulation history is available. We suppose that failure occurs when internal wear crosses a certain threshold or a traumatic event causes the item to fail. We model the infinitesimal increment in internal wear as a function of time, accumulated internal wear, and usage history, and we derive conditions on this function to get a collapsible model for the distribution of time to failure given the usage history. We reach the conclusion that collapsible models form the subset of accelerated failure time models with time-varying covariates for which the time transformation function satisfies certain simple properties.
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Liu, Xiaoqian, Zhenni Tan, Yuehua Wu, and Yong Zhou. "The Financial Risk Measurement EVaR Based on DTARCH Models." Entropy 25, no. 8 (2023): 1204. http://dx.doi.org/10.3390/e25081204.
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The value at risk based on expectile (EVaR) is a very useful method to measure financial risk, especially in measuring extreme financial risk. The double-threshold autoregressive conditional heteroscedastic (DTARCH) model is a valuable tool in assessing the volatility of a financial asset’s return. A significant characteristic of DTARCH models is that their conditional mean and conditional variance functions are both piecewise linear, involving double thresholds. This paper proposes the weighted composite expectile regression (WCER) estimation of the DTARCH model based on expectile regression theory. Therefore, we can use EVaR to predict extreme financial risk, especially when the conditional mean and the conditional variance of asset returns are nonlinear. Unlike the existing papers on DTARCH models, we do not assume that the threshold and delay parameters are known. Using simulation studies, it has been demonstrated that the proposed WCER estimation exhibits adequate and promising performance in finite samples. Finally, the proposed approach is used to analyze the daily Hang Seng Index (HSI) and the Standard & Poor’s 500 Index (SPI).
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Caroni, Chrys. "Regression Models for Lifetime Data: An Overview." Stats 5, no. 4 (2022): 1294–304. http://dx.doi.org/10.3390/stats5040078.
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Two methods dominate the regression analysis of time-to-event data: the accelerated failure time model and the proportional hazards model. Broadly speaking, these predominate in reliability modelling and biomedical applications, respectively. However, many other methods have been proposed, including proportional odds, proportional mean residual life and several other “proportional” models. This paper presents an overview of the field and the concept behind each of these ideas. Multi-parameter modelling is also discussed, in which (in contrast to, say, the proportional hazards model) more than one parameter of the lifetime distribution may depend on covariates. This includes first hitting time (or threshold) regression based on an underlying latent stochastic process. Many of the methods that have been proposed have seen little or no practical use. Lack of user-friendly software is certainly a factor in this. Diagnostic methods are also lacking for most methods.
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Chi, Dao Cai, Tao Tao Chen, Qian Liang, and Wei Chen. "Multi-Threshold Regression Reference Evapotranspiration Prediction Model Study Based on PSO Algorithm." Applied Mechanics and Materials 212-213 (October 2012): 25–32. http://dx.doi.org/10.4028/www.scientific.net/amm.212-213.25.
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In order to overcome the difficulties existing in estimating the multiple threshold values in multiple threshold regression models, a new Multi-threshold regression prediction method is proposed using Particle Swarm Optimization (PSO) in this paper, realizing estimating the multiple threshold value of multi-threshold regression (MTR) model. This method expands the prediction factors at the beginning, reduces the dimension using principal components analysis, searches threshold value by PSO and then establishes threshold regression model. A set of common modeling solutions of MTR is presented based on PSO at the same time. And this modeling solution is applied to the prediction of Reference Evapotranspiration (ET0) in the Panjin area. Results indicate this model has high precision for prediction and its application will provide a new means for predicting annual cumulative ET0.
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Vélez-Pereira, Andrés M., Concepción De Linares, Miquel A. Canela, and Jordina Belmonte. "A Comparison of Models for the Forecast of Daily Concentration Thresholds of Airborne Fungal Spores." Atmosphere 14, no. 6 (2023): 1016. http://dx.doi.org/10.3390/atmos14061016.
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Aerobiological predictive model development is of increasing interest, despite the distribution and variability of data and the limitations of statistical methods making it highly challenging. The use of concentration thresholds and models, where a binary response allows one to establish the occurrence or non-occurrence of the threshold, have been proposed to reduce difficulties. In this paper, we use logistic regression (logit) and regression trees to predict the daily concentration thresholds (low, medium, high, and very high) of six airborne fungal spore taxa (Alternaria, Cladosporium, Agaricus, Ganoderma, Leptosphaeria, and Pleospora) in eight localities in Catalonia (NE Spain) using data from 1995 to 2014. The predictive potential of these models was analyzed through sensitivity and specificity. The models showed similar results regarding the relationship and influence of the meteorological parameters and fungal spores. Ascospores showed a strong relationship with precipitation and basidiospores with minimum temperature, while conidiospores did not indicate any preferences. Sensitivity (true-positive) and specificity (false-positive) presented highly satisfactory validation results for both models in all thresholds, with an average of 73%. However, seeing as logit offers greater precision when attempting to establish the exceedance of a concentration threshold and is easier to apply, it is proposed as the best predictive model.
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Aldeyab, Mamoon A., Stuart E. Bond, Barbara R. Conway, Jade Lee-Milner, Jayanta B. Sarma та William J. Lattyak. "Identifying Antibiotic Use Targets for the Management of Antibiotic Resistance Using an Extended-Spectrum β-Lactamase-Producing Escherichia coli Case: A Threshold Logistic Modeling Approach". Antibiotics 11, № 8 (2022): 1116. http://dx.doi.org/10.3390/antibiotics11081116.
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The aim of this study was to develop a logistic modeling concept to improve understanding of the relationship between antibiotic use thresholds and the incidence of resistant pathogens. A combined approach of nonlinear modeling and logistic regression, named threshold logistic, was used to identify thresholds and risk scores in hospital-level antibiotic use associated with hospital-level incidence rates of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli (E. coli). Threshold logistic models identified thresholds for fluoroquinolones (61.1 DDD/1000 occupied bed days (OBD)) and third-generation cephalosporins (9.2 DDD/1000 OBD) to control hospital ESBL-producing E. coli incidence. The 60th percentile of ESBL-producing E. coli was determined as the cutoff for defining high incidence rates. Threshold logistic analysis showed that for every one-unit increase in fluoroquinolones and third-generation cephalosporins above 61.1 and 9.2 DDD/1000 OBD levels, the average odds of the ESBL-producing E. coli incidence rate being ≥60th percentile of historical levels increased by 4.5% and 12%, respectively. Threshold logistic models estimated the risk scores of exceeding the 60th percentile of a historical ESBL-producing E. coli incidence rate. Threshold logistic models can help hospitals in defining critical levels of antibiotic use and resistant pathogen incidence and provide targets for antibiotic consumption and a near real-time performance monitoring feedback system.
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Dong, Yingying, and Arthur Lewbel. "Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models." Review of Economics and Statistics 97, no. 5 (2015): 1081–92. http://dx.doi.org/10.1162/rest_a_00510.
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Matsuba, Ikuo. "Lagged regression, threshold models, and critical behavior near critical points of chaos." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 82, no. 5 (1999): 1–9. http://dx.doi.org/10.1002/(sici)1520-6440(199905)82:5<1::aid-ecjc1>3.0.co;2-m.
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Salsabila, Annisa Nur, and Siskarossa Ika Oktora. "Analysis of Underdeveloped Regency Using Logistic Threshold Regression Model." Jurnal Varian 8, no. 1 (2024): 55–66. https://doi.org/10.30812/varian.v8i1.3570.
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Regional development inequality causes some regions to lag behind other regions. An underdevelopedregency is a regency where territories and people are less developed than other regions nationally. Thegovernment has set a Human Development Index (HDI) target of 62.2 to 62.7 to accelerate the development of underdeveloped regency and prevent the regions from lagging. This study aims to evaluatethe HDI target and obtain the HDI value that reduces the risk of underdeveloped regency and acquiresvariables that affect underdeveloped regency’s status. The logistic threshold regression model is usedin this study with HDI as the threshold variable, 22 indicators for determining underdeveloped regencyas explanatory variables, and the underdeveloped regency’s status as the response variable. Thresholdregression can handle non-linear relationships between response and explanatory variables, includingvarious types of threshold models such as step, segmented, hinge, stegmented, and upper hinge. By applying a hinge threshold regression model using the R package ’chngpt,’ this study addresses non-linearrelationships and categorical responses. The results showed a threshold effect with a threshold value of62.9, indicating that the HDI target can reduce the region’s risk of being left behind.
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Muamba, Kalala Trésor. "Threshold Effect of Public Investment on Economic Growth in The Democratic Republic of Congo: Regime Change Approach." International Journal of Social Science and Human Research 08, no. 04 (2025): 2543–50. https://doi.org/10.5281/zenodo.15307804.
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This study analyzes the threshold effect of public investment on economic growth in the Democratic Republic of Congo (DRC), over the period from 1985 to 2022. Based on the threshold regression model of Hansen (1999, 2000) and the work of Barro (1990) and Aschauer (1989), it aims to identify a critical level of public investment beyond which its impact on economic growth changes in nature. The results show that when the public investment ratio is below 2.12%, it has a negative effect on growth, unlike private investment, which remains favorable. For a ratio between 2.12 and 4.1, both public and private investment stimulate growth. Above 4.1%, public investment continues to have a positive effect, while private investment loses statistical significance. The study highlights the importance of strategic management of public resources, in line with Barro's (1991) recommendations, to maximize the impact of investments on growth. Poor allocation of public funds can significantly reduce their economic return. Thus, strengthening the institutional framework and governance appears essential to improve the effectiveness of public investments and promote sustained and inclusive longterm growth in the DRC.
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Tran, Bao-Linh, Wei-Chun Tseng, Chi-Chung Chen, and Shu-Yi Liao. "Estimating the Threshold Effects of Climate on Dengue: A Case Study of Taiwan." International Journal of Environmental Research and Public Health 17, no. 4 (2020): 1392. http://dx.doi.org/10.3390/ijerph17041392.
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Climate change is regarded as one of the major factors enhancing the transmission intensity of dengue fever. In this study, we estimated the threshold effects of temperature on Aedes mosquito larval index as an early warning tool for dengue prevention. We also investigated the relationship between dengue vector index and dengue epidemics in Taiwan using weekly panel data for 17 counties from January 2012 to May 2019. To achieve our goals, we first applied the panel threshold regression technique to test for threshold effects and determine critical temperature values. Data were then further decomposed into different sets corresponding to different temperature regimes. Finally, negative binomial regression models were applied to assess the non-linear relationship between meteorological factors and Breteau index (BI). At the national level, we found that a 1°C temperature increase caused the expected value of BI to increase by 0.09 units when the temperature is less than 27.21 °C, and by 0.26 units when the temperature is greater than 27.21 °C. At the regional level, the dengue vector index was more sensitive to temperature changes because double threshold effects were found in the southern Taiwan model. For southern Taiwan, as the temperature increased by 1°C, the expected value of BI increased by 0.29, 0.63, and 1.49 units when the average temperature was less than 27.27 °C, between 27.27 and 30.17 °C, and higher than 30.17 °C, respectively. In addition, the effects of precipitation and relative humidity on BI became stronger when the average temperature exceeded the thresholds. Regarding the impacts of climate change on BI, our results showed that the potential effects on BI range from 3.5 to 54.42% under alternative temperature scenarios. By combining threshold regression techniques with count data regression models, this study provides evidence of threshold effects between climate factors and the dengue vector index. The proposed threshold of temperature could be incorporated into the implementation of public health measures and risk prediction to prevent and control dengue fever in the future.
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Duda, David P., and Patrick Minnis. "Basic Diagnosis and Prediction of Persistent Contrail Occurrence Using High-Resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part I: Effects of Random Error." Journal of Applied Meteorology and Climatology 48, no. 9 (2009): 1780–89. http://dx.doi.org/10.1175/2009jamc2056.1.
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Abstract Straightforward application of the Schmidt–Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper-tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy: the percent correct (PC) and the Hanssen–Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher (i.e., the forecasts are more skillful) when the climatological frequency of contrail occurrence is used as the critical threshold, whereas the PC scores are higher (i.e., the forecasts are more accurate) when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85% for the prediction of both contrail occurrence and nonoccurrence, although, in practice, larger errors would be anticipated.
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Berman, N. G., W. K. Wong, S. Bhasin, and E. Ipp. "Applications of segmented regression models for biomedical studies." American Journal of Physiology-Endocrinology and Metabolism 270, no. 4 (1996): E723—E732. http://dx.doi.org/10.1152/ajpendo.1996.270.4.e723.
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In many biological models, a relationship between variables may be modeled as a linear or polynomial function that changes abruptly when an independent variable obtains a threshold level. Usually, the transition point is unknown, and a major objective of the analysis is its estimation. This type of model is known as a segmented regression model. We present two methods, Gallant and Fuller's (J Am. Stat. Assoc. 68: 144-147, 1973) method and Tishler and Zang's (J. Am. Stat. Assoc. 76: 980-987, 1981) method, using nonlinear least-squares techniques for estimating the transition point. We give the following three examples: a hypoglycemia study, a testosterone study, and an estimate of age-cortisol relationship. Simulation techniques are used to compare the two methods. We conclude that these models provide useful information and that the two methods studied produce essentially equivalent results. We recommend that both methods be used to analyze a data set if possible to avoid problems due to local minima and that if the results do not agree, then evaluation of the likelihood function in the range of the estimates be used to determine the best estimate.
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Pan, Xiao, Gokhan Yildirim, Ataur Rahman, Khaled Haddad, and Taha B. M. J. Ouarda. "Peaks-Over-Threshold-Based Regional Flood Frequency Analysis Using Regularised Linear Models." Water 15, no. 21 (2023): 3808. http://dx.doi.org/10.3390/w15213808.
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Regional flood frequency analysis (RFFA) is widely used to estimate design floods in ungauged catchments. Most of the RFFA techniques are based on the annual maximum (AM) flood model; however, research has shown that the peaks-over-threshold (POT) model has greater flexibility than the AM model. There is a lack of studies on POT-based RFFA techniques. This paper presents the development of POT-based RFFA techniques, using regularised linear models (least absolute shrinkage and selection operator, ridge regression and elastic net regression). The results of these regularised linear models are compared with multiple linear regression. Data from 145 stream gauging stations of south-east Australia are used in this study. A leave-one-out cross-validation is adopted to compare these regression models. It has been found that the regularised linear models provide quite accurate flood quantile estimates, with a median relative error in the range of 37 to 47%, which outperform the AM-based RFFA techniques currently recommended in the Australian Rainfall and Runoff guideline. The developed RFFA technique can be used to estimate flood quantiles in ungauged catchments in the study region.
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Jennings, Jacob, Jay C. Perrett, Daniel W. Wundersitz, Courtney J. Sullivan, Stephen D. Cousins, and Michael I. Kingsley. "Predicting successful draft outcome in Australian Rules football: Model sensitivity is superior in neural networks when compared to logistic regression." PLOS ONE 19, no. 2 (2024): e0298743. http://dx.doi.org/10.1371/journal.pone.0298743.
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Using logistic regression and neural networks, the aim of this study was to compare model performance when predicting player draft outcome during the 2021 AFL National Draft. Physical testing, in-game movement and technical involvements were collected from 708 elite-junior Australian Rules football players during consecutive seasons. Predictive models were generated using data from 465 players (2017 to 2020). Data from 243 players were then used to prospectively predict the 2021 AFL National Draft. Logistic regression and neural network models were compared for specificity, sensitivity and accuracy using relative cut-off thresholds from 5% to 50%. Using factored and unfactored data, and a range of relative cut-off thresholds, neural networks accounted for 73% of the 40 best performing models across positional groups and data configurations. Neural networks correctly classified more drafted players than logistic regression in 88% of cases at draft rate (15%) and convergence threshold (35%). Using individual variables across thresholds, neural networks (specificity = 79 ± 13%, sensitivity = 61 ± 24%, accuracy = 76 ± 8%) were consistently superior to logistic regression (specificity = 73 ± 15%, sensitivity = 29 ± 14%, accuracy = 66 ± 11%). Where the goal is to identify talented players with draft potential, model sensitivity is paramount, and neural networks were superior to logistic regression.
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Qazi, Shakeel, Emmad Qazi, Alexis T. Wilson, et al. "Identifying Thrombus on Non-Contrast CT in Patients with Acute Ischemic Stroke." Diagnostics 11, no. 10 (2021): 1919. http://dx.doi.org/10.3390/diagnostics11101919.
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The hyperdense sign is a marker of thrombus in non-contrast computed tomography (NCCT) datasets. The aim of this work was to determine optimal Hounsfield unit (HU) thresholds for thrombus segmentation in thin-slice non-contrast CT (NCCT) and use these thresholds to generate 3D thrombus models. Patients with thin-slice baseline NCCT (≤2.5 mm) and MCA-M1 occlusions were included. CTA was registered to NCCT, and three regions of interest (ROIs) were placed in the NCCT, including: the thrombus, contralateral brain tissue, and contralateral patent MCA-M1 artery. Optimal HU thresholds differentiating the thrombus from non-thrombus tissue voxels were calculated using receiver operating characteristic analysis. Linear regression analysis was used to predict the optimal HU threshold for discriminating the clot only based on the average contralateral vessel HU or contralateral parenchyma HU. Three-dimensional models from 70 participants using standard (45 HU) and patient-specific thresholds were generated and compared to CTA clot characteristics. The optimal HU threshold discriminating thrombus in NCCT from other structures varied with a median of 51 (IQR: 49–55). Experts chose 3D models derived using patient-specific HU models as corresponding better to the thrombus seen in CTA in 83.8% (31/37) of cases. Patient-specific HU thresholds for segmenting the thrombus in NCCT can be derived using normal parenchyma. Thrombus segmentation using patient-specific HU thresholds is superior to conventional 45 HU thresholds.
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Ben Abdallah, Amal, Hamdi Becha, Maha Kalai, and Kamel Helali. "Development of Digital Financial Inclusion in China's Regional Economy: Evidence from Panel Threshold Models." Journal of Telecommunications and the Digital Economy 12, no. 1 (2024): 637–56. http://dx.doi.org/10.18080/jtde.v12n1.838.
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This study aims to investigate the effect of digital financial inclusion and air pollution on economic growth for 31 Chinese provinces between 2003 and 2022 using Panel Threshold Auto-Regressive (PTAR) and Panel Smooth Transition Auto-Regression (PSTAR) models. The results show that there is a nonlinear link between digital financial inclusion and economic growth in China. For PTAR, the LnDFII thresholds are 4.264 (i.e., DFII = 71.094), and for PSTAR are 4.563 (i.e., DFII = 95.871). Below these thresholds, digital financial inclusion significantly boosts economic growth by 0.061 and 0.063 in the PTAR and PSTAR models, respectively. However, above these thresholds, the positive impact diminishes, with coefficients dropping to 0.015 and 0.004 in the PTAR and PSTAR models, respectively. Additionally, both models indicate that digital financial inclusion positively affects reducing air pollution, thereby potentially fostering economic growth. Hence, authorities should strategically implement digital technologies and strengthen collaborative efforts at the regional level to maximize these benefits.
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Mallidis, Ioannis, Volha Yakavenka, Anastasios Konstantinidis, and Nikolaos Sariannidis. "A Goal Programming-Based Methodology for Machine Learning Model Selection Decisions: A Predictive Maintenance Application." Mathematics 9, no. 19 (2021): 2405. http://dx.doi.org/10.3390/math9192405.
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The paper develops a goal programming-based multi-criteria methodology, for assessing different machine learning (ML) regression models under accuracy and time efficiency criteria. The developed methodology provides users with high flexibility in assessing the models as it allows for a fast and computationally efficient sensitivity analysis of accuracy and time significance weights as well as accuracy and time significance threshold values. Four regression models were assessed, namely the decision tree, random forest, support vector and the neural network. The developed methodology was employed to forecast the time to failures of NASA Turbofans. The results reveal that decision tree regression (DTR) seems to be preferred for low values of accuracy weights (up to 30%) and low accuracy and time efficiency threshold values. As the accuracy weights tend to increase and for higher accuracy and time efficiency threshold values, random forest regression (RFR) seems to be the best choice. The preference for the RFR model however, seems to change towards the adoption of the neural network for accuracy weights equal to and higher than 90%.
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Manurung, Monika Stevany, and Aisyah Fitri Yuniasih. "Estimation of Inflation Threshold of Indonesia and Its Effect on Economic Growth Periode 1981-2019." Proceedings of The International Conference on Data Science and Official Statistics 2021, no. 1 (2022): 683–90. http://dx.doi.org/10.34123/icdsos.v2021i1.230.
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Sustainable economic growth with a low and stable inflation rate is one of the goals of macroeconomic policy in improving people's welfare. High inflation can be detrimental to economic growth in the medium and long term, while a certain level of inflation is needed to move the economy. Therefore, the question arises about the level of inflation that does not have a negative impact on economic growth. This study aims to estimate the inflation threshold level and identify its effect on Indonesia's economic growth 1981-2019. The research begins by determining the best model among the models that regress inflation on economic growth with quadratic regression, Hansen's (2000) threshold regression, and Mubarik's (2005) threshold regression (2005). The best model is the Mubarik threshold regression model (2005) with an inflation threshold of 6.85 percent. Mubarik's (2005) threshold regression analysis was reused in the model involving the FDI variable, the inflation threshold was 7.11 percent, and FDI had a positive effect. Inflation below the inflation threshold encourages economic growth, while inflation above the inflation threshold is detrimental to economic growth. The result of the estimated threshold level is higher than the inflation target by BI, so that inflation targeting can be increased.
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Ishaku, Rimamtanung Nyiputen, and Magaji Ibrahim Yakubu. "Monetary Policy Rate and Market Interest Rates in Nigeria." Proceedings International Conference on Business, Economics & Management, no. 2 (January 8, 2025): 54–73. https://doi.org/10.47747/icbem.v2i2.2547.
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The impact of Nigeria's market interest rates on the monetary policy rate (MPR) is investigated in this study. In order to maintain parsimony, we create two indexes to represent deposit and lending rates, respectively: the short-term interest rate (SINT) and the lending interest rate (LINT). The models used are threshold regression and nonlinear autoregressive distributed lag (NARDL). Monthly data from 2002:M1 to 2019:M12 are used in the study. According to the threshold regression model's results, MPR has a more substantial and larger impact on SINT and LINT over the projected thresholds of 11 and 13 percent, respectively, than it would if it were below the threshold. Additionally, results from the nonlinear ARDL model demonstrate that a drop in MPR has a negative impact on lending and short-term interest rates, whereas an increase in MPR has a favorable effect. The extent of the negative effect is negligible for LINT and statistically insignificant for SINT. This illustrates how prices are sticky downward, supporting the claim that MPR is only ineffective when modified downward. To increase the effectiveness of monetary policy, we advise the monetary authority to concentrate on banking sector reforms that eliminate downward rigidities in the impact of MPR on interest rates.
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Hossain, Shamima. "Generalized Linear Regression Model to Determine the Threshold Effects of Climate Variables on Dengue Fever: A Case Study on Bangladesh." Canadian Journal of Infectious Diseases and Medical Microbiology 2023 (April 24, 2023): 1–12. http://dx.doi.org/10.1155/2023/2131801.
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One of the leading causes of the increase in the intensity of dengue fever transmission is thought to be climate change. Examining panel data from January 2000 to December 2021, this study discovered the nonlinear relationship between climate variables and dengue fever cases in Bangladesh. To determine this relationship, in this study, the monthly total rainfall in different years has been divided into two thresholds: (90 to 360 mm) and (<90 or >360 mm), and the daily average temperature in different months of the different years has been divided into four thresholds: (16°C to ≤20°C), (>20°C to ≤25°C), (>25°C to ≤28°C), and (>28°C to ≤30°C). Then, quasi-Poisson and zero-inflated Poisson regression models were applied to assess the relationship. This study found a positive correlation between temperature and dengue incidence and furthermore discovered that, among those four average temperature thresholds, the total number of dengue cases is maximum if the average temperature falls into the threshold (>28°C to ≤30°C) and minimum if the average temperature falls into the threshold (16°C to ≤20°C). This study also discovered that between the two thresholds of monthly total rainfall, the risk of a dengue fever outbreak is approximately two times higher when the monthly total rainfall falls into the thresholds (90 mm to 360 mm) compared to the other threshold. This study concluded that dengue fever incidence rates would be significantly more affected by climate change in regions with warmer temperatures. The number of dengue cases rises rapidly when the temperature rises in the context of moderate to low rainfall. This study highlights the significance of establishing potential temperature and rainfall thresholds for using risk prediction and public health programs to prevent and control dengue fever.
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Nersisyan, Stepan, Victor Novosad, Alexei Galatenko, et al. "ExhauFS: exhaustive search-based feature selection for classification and survival regression." PeerJ 10 (March 30, 2022): e13200. http://dx.doi.org/10.7717/peerj.13200.
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Feature selection is one of the main techniques used to prevent overfitting in machine learning applications. The most straightforward approach for feature selection is an exhaustive search: one can go over all possible feature combinations and pick up the model with the highest accuracy. This method together with its optimizations were actively used in biomedical research, however, publicly available implementation is missing. We present ExhauFS—the user-friendly command-line implementation of the exhaustive search approach for classification and survival regression. Aside from tool description, we included three application examples in the manuscript to comprehensively review the implemented functionality. First, we executed ExhauFS on a toy cervical cancer dataset to illustrate basic concepts. Then, multi-cohort microarray breast cancer datasets were used to construct gene signatures for 5-year recurrence classification. The vast majority of signatures constructed by ExhauFS passed 0.65 threshold of sensitivity and specificity on all datasets, including the validation one. Moreover, a number of gene signatures demonstrated reliable performance on independent RNA-seq dataset without any coefficient re-tuning, i.e., turned out to be cross-platform. Finally, Cox survival regression models were used to fit isomiR signatures for overall survival prediction for patients with colorectal cancer. Similarly to the previous example, the major part of models passed the pre-defined concordance index threshold 0.65 on all datasets. In both real-world scenarios (breast and colorectal cancer datasets), ExhauFS was benchmarked against state-of-the-art feature selection models, including L1-regularized sparse models. In case of breast cancer, we were unable to construct reliable cross-platform classifiers using alternative feature selection approaches. In case of colorectal cancer not a single model passed the same 0.65 threshold. Source codes and documentation of ExhauFS are available on GitHub: https://github.com/s-a-nersisyan/ExhauFS.
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KURUL, Zühal. "The Effects of Financial Development on Trade Openness: Evidence from Panel Threshold Regression Models." Bulletin of Economic Theory and Analysis 6, no. 1 (2021): 53–68. http://dx.doi.org/10.25229/beta.887369.
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Tsionas, Efthymios G., and Dimitris K. Christopoulos. "Maastricht convergence and real convergence: European evidence from threshold and smooth transition regression models." Journal of Policy Modeling 25, no. 1 (2003): 43–52. http://dx.doi.org/10.1016/s0161-8938(02)00200-4.
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Zhang, Qian, Huaxing Zhang, Dan Zhao, Baodong Cheng, Chang Yu, and Yanli Yang. "Does Urban Sprawl Inhibit Urban Eco-Efficiency? Empirical Studies of Super-Efficiency and Threshold Regression Models." Sustainability 11, no. 20 (2019): 5598. http://dx.doi.org/10.3390/su11205598.
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With rapid urbanization in China, the phenomenon of urban sprawl has become prominent and has severely challenged sustainable urbanization and ecological civilization. Aiming to understand the impact of urban sprawl on the urban environment, this study calculates the eco-efficiency of 264 prefecture-level cities in China from 2003 to 2016 by using a super-efficiency data envelopment analysis model. Then, we establish a panel Tobit model and threshold regression model to empirically test the impact of urban sprawl on eco-efficiency and the threshold effect of the urban scale. The results show that urban sprawl hinders the improvement of urban eco-efficiency, especially in Eastern China, but relatively weak or even insignificant effects are observed in Central and Western China. Additionally, a threshold effect of urban sprawl on eco-efficiency can be found. When the city scale is small, urban sprawl seriously hinders the improvement of eco-efficiency. As the city scale gradually expands, the negative effect of urban sprawl on eco-efficiency first decreases, then the restraining effect is gradually strengthened. Our research findings can aid urban development in cities with different scales to reduce the negative effect of urban sprawl on the urban environment.
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Bazrkar, Mohammad Hadi, and Xuefeng Chu. "Development of category-based scoring support vector regression (CBS-SVR) for drought prediction." Journal of Hydroinformatics 24, no. 1 (2022): 202–22. http://dx.doi.org/10.2166/hydro.2022.104.
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Abstract Using the existing measures for training numerical (non-categorical) prediction models can cause misclassification of droughts. Thus, developing a drought category-based measure is critical. Moreover, the existing fixed drought category thresholds need to be improved. The objective of this research is to develop a category-based scoring support vector regression (CBS-SVR) model based on an improved drought categorization method to overcome misclassification in drought prediction. To derive variable threshold levels for drought categorization, K-means (KM) and Gaussian mixture (GM) clustering are compared with the traditional drought categorization. For drought prediction, CBS-SVR is performed by using the best categorization method. The new drought model was applied to the Red River of the North Basin (RRB) in the USA. In the model training and testing, precipitation, temperature, and actual evapotranspiration were selected as the predictors, and the target variables consisted of multivariate drought indices, as well as bivariate and univariate standardized drought indices. Results indicated that the drought categorization method, variable threshold levels, and the type of drought index were the major factors that influenced the accuracy of drought prediction. The CBS-SVR outperformed the support vector classification and traditional SVR by avoiding overfitting and miscategorization in drought prediction.
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Ortiz-Rodríguez, Damian O., Antoine Guisan, and Maarten J. Van Strien. "Sensitivity of habitat network models to changes in maximum dispersal distance." PLOS ONE 18, no. 11 (2023): e0293966. http://dx.doi.org/10.1371/journal.pone.0293966.
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Predicting the presence or absence (occurrence-state) of species in a certain area is highly important for conservation. Occurrence-state can be assessed by network models that take suitable habitat patches as nodes, connected by potential dispersal of species. To determine connections, a connectivity threshold is set at the species’ maximum dispersal distance. However, this requires field observations prone to underestimation, so for most animal species there are no trustable maximum dispersal distance estimations. This limits the development of accurate network models to predict species occurrence-state. In this study, we performed a sensitivity analysis of the performance of network models to different settings of maximum dispersal distance. Our approach, applied on six amphibian species in Switzerland, used habitat suitability modelling to define habitat patches, which were linked within a dispersal distance threshold to form habitat networks. We used network topological measures, patch suitability, and patch size to explain species occurrence-state in habitat patches through boosted regression trees. These modelling steps were repeated on each species for different maximum dispersal distances, including a species-specific value from literature. We evaluated mainly the predictive performance and predictor importance among the network models. We found that predictive performance had a positive relation with the distance threshold, and that almost none of the species-specific values from literature yielded the best performance across tested thresholds. With increasing dispersal distance, the importance of the habitat-quality-related variable decreased, whereas that of the topology-related predictors increased. We conclude that the sensitivity of these models to the dispersal distance parameter stems from the very different topologies formed with different movement assumptions. Most reported maximum dispersal distances are underestimated, presumably due to leptokurtic dispersal distribution. Our results imply that caution should be taken when selecting a dispersal distance threshold, considering higher values than those derived from field reports, to account for long-distance dispersers.
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Yaşar, Zaim Reha. "Asymmetric Effects of Real Exchange Rate on Turkey's Imports: Threshold Value Regression Model." Bulletin of Economic Theory and Analysis 9, no. 3 (2024): 787–808. http://dx.doi.org/10.25229/beta.1504315.
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Determinants of imports are one of the most discussed topics in the foreign trade literature. This study explores the asymmetric relationship between the real exchange rate and imports for Turkey example. The paper employs monthly data spanning from 2013:01 to 2023:06, estimating classical least square estimation and Threshold Regression Models, subsequently comparing their outcomes. The findings highlighted that the relations between imports and real exchange rate are asymmetric. According to the least square estimation model, it was noted that the real exchange rate had a negative impact on imports, aligning with anticipated outcomes. In the threshold regression model, it was seen that movements in the real exchange rate positively affected imports in the model below the threshold, while exerting a negative effect in the model above the threshold. This finding was interpreted as increasing imports by buying the expectation that the exchange rate increases, which are below the internalizable level, may increase further. It is considered essential for policymakers to take into account asymmetric relationships when analyzing the relationships between imports and the exchange rate.
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Amris, Kirstine, Eva Ejlersen Wæhrens, Anders Jespersen, et al. "The Relationship between Mechanical Hyperalgesia Assessed by Manual Tender Point Examination and Disease Severity in Patients with Chronic Widespread Pain: A Cross-Sectional Study." International Journal of Rheumatology 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/417596.
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The clinical utility of tender point (TP) examination in patients reporting chronic widespread pain (CWP) is the subject of contemporary debate. The objective of this study was to assess the relationship between mechanical hyperalgesia assessed by manual TP examination and clinical disease severity. 271 women with CWP were recruited from a clinical setting. Data collection included patient-reported symptoms, health-related quality of life variables, and observation-based measures of functional ability, muscle strength, 6-minute walk, and pressure pain thresholds measured by cuff algometry. TP examination was conducted according to ACR-guidelines. Relationships between disease variables and TP count (TPC) were analyzed with logistic regression in a continuum model, allowing the TPC to depend on the included disease variables and two regression models carried out for a TPC threshold level, varying between 1 and 17. The threshold analyses indicated a TPC threshold at 8, above which a large number of disease variables became consistently significant explanatory factors, whereas none of the disease variables reached a significance level in the continuum model. These results support the premise that the presence of mechanical hyperalgesia influences symptomatology in CWP and that the severity of clinical expression is related to a threshold of TPs, rather than being part of a continuum.
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Abdulqadir, Idris Abdullahi, and Soo Y. Chua. "Asymmetric impact of exchange rate pass-through into employees' wages in sub-Saharan Africa: panel non-linear threshold estimation." Journal of Economic Studies 47, no. 7 (2020): 1629–47. http://dx.doi.org/10.1108/jes-03-2019-0128.
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PurposeThe purpose of this article is to investigate the asymmetric impact of exchange rate pass-through (ERPT) on employees' wages via consumer prices in 15 major oil-exporting countries from sub-Saharan Africa over the period 1996-2017 using the panel threshold regression model.Design/methodology/approachThe methodology used in this article was built on non-linear panel threshold regression models developed by Hansen (1996, 1999) threshold regression. The authors first tested for the existence of threshold-effect in ERPT and wage nexus using 1,000 bootstrap replications and 400 grid searches to obtain an optimal threshold. We also estimated that asymmetric ERPT on employees' wages reacts differently when the inflation-threshold exceeds beyond a 15.12% threshold level.FindingsOur findings showed that asymmetric ERPT is incomplete and indicates that an increase by one standard deviation in real exchange rate causes a decline in employees' wages by 2.69%.Research limitations/implicationsThe policy implications of our results are drawn from the significant threshold estimates. However, a significant threshold value of 15.12 is an inflation-threshold estimates that split our 330 observations into the lower (upper) regimes. Further, an inflation rate beyond the threshold value is likely to have an asymmetric ERPT on employees' wages in the 15 major oil-exporting sub-Saharan African (SSA) countries.Practical implicationsThe practical implication of the study is when ERPT exceeds the threshold, the effect of real exchange rate variations is passed on to employees' wages. It is widely believed that labor productivity increase with increased minimum wages. Nevertheless, there is contention as regards the effects on employment and poverty. As rising goods prices make the minimum wage increased homogeneous of degree zero.Social implicationsConsiderable increased ERPT on imported goods reduces employees' wages purchasing ability from import-dependent countries through import prices. Once it has documented, this also reduces welfare via deteriorations of marginal propensity to consume (MPC) and marginal propensity to savings (MPS).Originality/valueThis article integrates labor purchasing power into the analysis of ERPT using non-linear dynamic panel heterogeneous threshold regression. It extends the Hansen (1996, 1999) dynamic panel threshold models to exchange rate pass-through in SSA economies.
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Park, N. R., H. S. Yun, and C. H. Choi. "Green Trade and Cultural Innovation: Examining the Impact on GTFP and Greenhouse Gas Emissions in OECD Countries." Sustainability 16, no. 19 (2024): 8339. http://dx.doi.org/10.3390/su16198339.
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This study investigates the impact of green trade exports (GTE) on green total factor productivity (GTFP) and environmental sustainability in OECD countries using panel data from 2003 to 2016. We employ linear regression models, polynomial models, and threshold regression techniques to analyze this relationship. Our findings reveal an inverted N-shaped curve between GTE and GTFP, with two turning points. The threshold regression results indicate that when clean energy is below 8.660%, a 1 unit increase in GTE decreases GTFP by 1.518 units. However, when clean energy exceeds this threshold, a 1 unit increase in GTE increases GTFP by 1.056 units. For R&D, the effect of GTE on GTFP changes from −3.165 to 1.193 as R&D exceeds the threshold of 0.664%. Additionally, we find that GTE has a lagged effect on increasing CO2 emissions, with coefficients of 0.0234 and 0.0278 for 1-year and 2-year lags, respectively. The interaction between clean energy and GTE reduces CO2 emissions by 0.00234 units and PM2.5 by 0.0145 units. These results provide important insights for policymakers in developing countries seeking to participate effectively in the global industrial chain while promoting sustainable development.
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de Oliveira, Lorena Tavares, Cristina Moreira Bonafé, Fabyano Fonseca e Silva, et al. "Bayesian random regression threshold models for genetic evaluation of pregnancy probability in Red Sindhi heifers." Livestock Science 202 (August 2017): 166–70. http://dx.doi.org/10.1016/j.livsci.2017.06.005.
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Guo, Yuanzhi, and Wenyue Zhong. "How urbanization shapes rural ageing in China? Evidence from spatial Durbin and threshold regression models." Habitat International 163 (September 2025): 103487. https://doi.org/10.1016/j.habitatint.2025.103487.
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Loranger, Jean-Guy. "Modèle de régression avec variables d’écart." Articles 50, no. 2 (2009): 177–90. http://dx.doi.org/10.7202/803042ar.
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Abstract A look at M. G. Dagenais' contributions (1969, 1973) on threshold regression models and at chapter 9 of S.M. Goldfeld and R.E. Quandt's book (1972) concerning switching regression models suggested to me that a new approach to estimating the threshold model by introducing slack variables might be possible. One of the main advantages of this new method is to simplify to a great extent the estimation of the likelihood function which is reduced partly to the problem of estimating a limited number of simple integrals for each iteration in the process of optimization. In order to facilitate a better understanding of our approach, two main models will be reviewed in the next section: the twin linear probability model (which can be estimated either by OLS, by a combination of probit and OLS, or by the tobit approach) and the threshold model. A critical look at the empirical results obtained by Dagenais (1973) will also be made before closing this section. Our new threshold model with slack variables is presented in section 3 and the main features of our new approach are summarized in the last section of this paper.
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Shin, Juyoung, Joonyub Lee, Taehoon Ko, Kanghyuck Lee, Yera Choi, and Hun-Sung Kim. "Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness." Journal of Personalized Medicine 12, no. 11 (2022): 1899. http://dx.doi.org/10.3390/jpm12111899.
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The early prediction of diabetes can facilitate interventions to prevent or delay it. This study proposes a diabetes prediction model based on machine learning (ML) to encourage individuals at risk of diabetes to employ healthy interventions. A total of 38,379 subjects were included. We trained the model on 80% of the subjects and verified its predictive performance on the remaining 20%. Furthermore, the performances of several algorithms were compared, including logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), Cox regression, and XGBoost Survival Embedding (XGBSE). The area under the receiver operating characteristic curve (AUROC) of the XGBoost model was the largest, followed by those of the decision tree, logistic regression, and random forest models. For the survival analysis, XGBSE yielded an AUROC exceeding 0.9 for the 2- to 9-year predictions and a C-index of 0.934, while the Cox regression achieved a C-index of 0.921. After lowering the threshold from 0.5 to 0.25, the sensitivity increased from 0.011 to 0.236 for the 2-year prediction model and from 0.607 to 0.994 for the 9-year prediction model, while the specificity showed negligible changes. We developed a high-performance diabetes prediction model that applied the XGBSE algorithm with threshold adjustment. We plan to use this prediction model in real clinical practice for diabetes prevention after simplifying and validating it externally.
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Guerra-Hernández, J., M. Tomé, and E. González-Ferreiro. "Using low density LiDAR data to map Mediterranean forest characteristics by means of an area-based approach and height threshold analysis." Revista de Teledetección, no. 46 (June 27, 2016): 103. http://dx.doi.org/10.4995/raet.2016.3980.
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<p>This study reports progress in forest inventory methods involving the use of low density airborne LiDAR data and an area-based approach (ABA). It also emphasizes the usefulness of the Spanish countrywide LiDAR dataset for mapping forest stand attributes in Mediterranean stone pine forest characterized by complex orography. Lowdensity airborne LiDAR data (0.5 first returns m<sup><span lang="EN-US">–2</span></sup>) was used to develop individual regression models for a set of forest stand variables in different types of forest. LiDAR data is now freely available for most of the Spanish territory and is provided by the Spanish National Aerial Photography Program (Plan Nacional de Ortofotografía Aérea, PNOA). The influence of height thresholds (MHT: Minimun Height Threshold and BHT: Break Height Threshold) used in extracting LiDAR metrics was also investigated. The best regression models explained 61-85%, 67-98% and 74-98% of the variability in ground-truth stand height, basal area and volume, respectively. The magnitude of error for predicting structural vegetation parameters was higher in closed deciduous and mixed forest than in the more homogeneous coniferous stands. Analysis of height thresholds (HT) revealed that these parameters were not particularly important for estimating several forest attributes in the coniferous forest; nevertheless, substantial differences in volume modelling were observed when the height thresholds (MHT and BHT) were increased in complex structural vegetation (mixed and deciduous forest). A metric-by-metric analysis revealed that there were significant differences in most of the explanatory variables computed from different height thresholds (HBT and MHT).The best models were applied to the reference stands to yield spatially explicit predictions about the forest resources. Reliable mapping of biometric variables was implemented to facilitate effective and sustainable management strategies and practices in Mediterranean Forest ecosystems.</p>