Quantile Values Research Articles

Head CT: Toward Making Full Use of the Information the X-Rays Give.

Although clinical head CT images are typically interpreted qualitatively, automated methods applied to routine clinical head CTs enable quantitative assessment of brain volume, brain parenchymal fraction, brain radiodensity, and brain radiomass. These metrics gain clinical meaning when viewed relative to a reference database and expressed as quantile regression values. Quantitative imaging data can aid in objective reporting and in the identification of outliers, with possible diagnostic implications. The comparison to a reference database necessitates standardization of head CT imaging parameters and protocols. Future research is needed to learn the effects of virtual monochromatic imaging on the quantitative characteristics of head CT images.

Copula-based multiple indicator kriging for non-Gaussian random fields

In spatial statistics, the kriging predictor is the best linear predictor at unsampled locations, but not the optimal predictor for non-Gaussian processes. In this paper, we introduce a copula-based multiple indicator kriging model for the analysis of non-Gaussian spatial data by thresholding the spatial observations at a given set of quantile values. The proposed copula model allows for flexible marginal distributions while modeling the spatial dependence via copulas. We show that the covariances required by kriging have a direct link to the chosen copula function. We then develop a semiparametric estimation procedure. The proposed method provides the entire predictive distribution function at a new location, and thus allows for both point and interval predictions. The proposed method demonstrates better predictive performance than the commonly used variogram approach and Gaussian kriging in the simulation studies. We illustrate our methods on precipitation data in Spain during November 2019, and heavy metal dataset in topsoil along the river Meuse, and obtain probability exceedance maps.

Prognostic relevance of elevated plasma osmolality on admission in acute decompensated heart failure with preserved ejection fraction: insights from PURSUIT-HFpEF registry

BackgroundComplicated pathophysiology makes it difficult to identify the prognosis of heart failure with preserved ejection fraction (HFpEF). While plasma osmolality has been reported to have prognostic importance, mainly in heart failure with reduced ejection fraction (HFrEF), its prognostic meaning for HFpEF has not been elucidated.MethodsWe prospectively studied 960 patients in PURSUIT-HFpEF, a multicenter observational study of acute decompensated HFpEF inpatients. We divided patients into three groups according to the quantile values of plasma osmolality on admission. During a follow-up averaging 366 days, we examined the primary composite endpoint of cardiac mortality or heart failure re-admission using Kaplan–Meier curve analysis and Cox proportional hazard testing.Results216 (22.5%) patients reached the primary endpoint. Kaplan–Meier curve analysis revealed that the highest quantile of plasma osmolality on admission (higher than 300.3 mOsm/kg) was significantly associated with adverse outcomes (Log-rank P = 0.0095). Univariable analysis in the Cox proportional hazard model also revealed significantly higher rates of adverse outcomes in the higher plasma osmolality on admission (hazard ratio [HR] 7.29; 95% confidence interval [CI] 2.25–23.92, P = 0.0009). Multivariable analysis in the Cox proportional hazard model also showed that higher plasma osmolality on admission was significantly associated with adverse outcomes (HR 5.47; 95% CI 1.46–21.56, P = 0.0113) independently from other confounding factors such as age, gender, comorbid of atrial fibrillation, hypertension history, diabetes, anemia, malnutrition, E/e′, and N-terminal pro-B-type natriuretic peptide elevation.ConclusionsHigher plasma osmolality on admission was prognostically important for acute decompensated HFpEF inpatients.

Quantile regression modeling to predict extreme precipitation

Quantile regression is an extension of the median regression that analyzes various quantile values. This method is used to predict the relationship between the response variable (Y) and the predictor variable (X) on the conditional quantile function. Quantile regression can be used to detect extreme conditions, either extreme dry (quantile 5) or extreme wet (quantile 95). Extreme precipitation often occurs in Indonesian territory because the area is surrounded by oceans. High frequency of extreme precipitation may trigger disasters, one of which is flooding. In 2017, there were floods in Sidoarjo area with a loss of up to 2 billion. In an effort to anticipate the adverse effects of extreme precipitation, forecast information abaut extreme precipitation is needed, one of which is using quantile regression. The objectives of this research was to determine the best quantile regression model for predict extreme precitipation. The data of this study were secondary data obtained from BMKG with a data length of 30 years. Modeling of extreme precipitation in Sidoarjo area involved variables of humidity, temperature and air pressure for model 1. Whereas model 2 involved 3 variables in model 1 plus the month and month square variables. The addition of the month variable and the month quadratic variable in model 2 was based on the precipitation data plot which formed a quadratic trend. Based on the Pseudo R2 value, it can be concluded that the best model to predict extreme precipitation in Sidoarjo is Model 2.

Additive Ensemble Neural Network with Constrained Weighted Quantile Loss for Probabilistic Electric-Load Forecasting.

This work proposes a quantile regression neural network based on a novel constrained weighted quantile loss (CWQLoss) and its application to probabilistic short and medium-term electric-load forecasting of special interest for smart grids operations. The method allows any point forecast neural network based on a multivariate multi-output regression model to be expanded to become a quantile regression model. CWQLoss extends the pinball loss to more than one quantile by creating a weighted average for all predictions in the forecast window and across all quantiles. The pinball loss for each quantile is evaluated separately. The proposed method imposes additional constraints on the quantile values and their associated weights. It is shown that these restrictions are important to have a stable and efficient model. Quantile weights are learned end-to-end by gradient descent along with the network weights. The proposed model achieves two objectives: (a) produce probabilistic (quantile and interval) forecasts with an associated probability for the predicted target values. (b) generate point forecasts by adopting the forecast for the median (0.5 quantiles). We provide specific metrics for point and probabilistic forecasts to evaluate the results considering both objectives. A comprehensive comparison is performed between a selection of classic and advanced forecasting models with the proposed quantile forecasting model. We consider different scenarios for the duration of the forecast window (1 h, 1-day, 1-week, and 1-month), with the proposed model achieving the best results in almost all scenarios. Additionally, we show that the proposed method obtains the best results when an additive ensemble neural network is used as the base model. The experimental results are drawn from real loads of a medium-sized city in Spain.

An order independent algorithm for inferring gene regulatory network using quantile value for conditional independence tests

In recent years, due to the difficulty and inefficiency of experimental methods, numerous computational methods have been introduced for inferring the structure of Gene Regulatory Networks (GRNs). The Path Consistency (PC) algorithm is one of the popular methods to infer the structure of GRNs. However, this group of methods still has limitations and there is a potential for improvements in this field. For example, the PC-based algorithms are still sensitive to the ordering of nodes i.e. different node orders results in different network structures. The second is that the networks inferred by these methods are highly dependent on the threshold used for independence testing. Also, it is still a challenge to select the set of conditional genes in an optimal way, which affects the performance and computation complexity of the PC-based algorithm. We introduce a novel algorithm, namely Order Independent PC-based algorithm using Quantile value (OIPCQ), which improves the accuracy of the learning process of GRNs and solves the order dependency issue. The quantile-based thresholds are considered for different orders of CMI tests. For conditional gene selection, we consider the paths between genes with length equal or greater than 2 while other well-known PC-based methods only consider the paths of length 2. We applied OIPCQ on the various networks of the DREAM3 and DREAM4 in silico challenges. As a real-world case study, we used OIPCQ to reconstruct SOS DNA network obtained from Escherichia coli and GRN for acute myeloid leukemia based on the RNA sequencing data from The Cancer Genome Atlas. The results show that OIPCQ produces the same network structure for all the permutations of the genes and improves the resulted GRN through accurately quantifying the causal regulation strength in comparison with other well-known PC-based methods. According to the GRN constructed by OIPCQ, for acute myeloid leukemia, two regulators BCLAF1 and NRSF reported previously are significantly important. However, the highest degree nodes in this GRN are ZBTB7A and PU1 which play a significant role in cancer, especially in leukemia. OIPCQ is freely accessible at https://github.com/haammim/OIPCQ-and-OIPCQ2.

Investigating distribution of nitrate concentration using ensemble nonparametric quantile regression

Abstract Nitrate ( NO3−) pollution in groundwater is a major concern due to its negative health effects; therefore, accurately estimating and predicting the NO3− concentration in groundwater is necessary. The NO3− concentration distribution can be used to find less polluted areas, and these identified areas can be candidates for drinking water resources. We considered a total of 14,297 NO3− concentration observations in South Korea. Altitude, slope, land use, hydrogeological unit, and surface soil texture data were also collected to assess the covariates that affect NO3− concentration levels. Sample quantiles display nonlinear patterns based on these covariates. Thus, we propose using an ensemble nonparametric quantile regression approach to determine the NO3− concentration distribution. The proposed approach is a data-driven method that ensembles nonlinear quantile models to capture the complex relationships between quantiles of the response variable and the covariates while controlling the computational complexity, which are advantages over non-ensemble quantile regression methods. The validation study demonstrates that the proposed method exhibits a smaller loss value compared to that of the non-ensemble models considered for comparison. We investigated lower quantile maps of NO3− concentration (5% and 10%), as we are interested in less polluted areas. The proposed model attempts to reach the sample quantile values while a non-ensemble nonlinear model with altitude and slope does not except land use is coniferous at low altitude. We created a proportional map with less polluted areas at the district level using the proposed approach. This provided us with the top five districts (Samcheok City, Geochang County, Yanggu County, Inje County, and Yeongwol County) with the highest proportions of less-polluted areas in South Korea.

Analysis of pediatricians’ knowledge about autism

Background/Aim: In autism spectrum disorders (ASD), early diagnosis is important for treatment, and pediatricians are health professionals who are likely to encounter ASD at the earliest stages. In this study, we aimed to examine the information sources of pediatricians, their current knowledge about autism and the affecting factors. Methods: The study was conducted as an online cross-sectional self-report questionnaire and the data of 145 pediatricians were analyzed. The sociodemographic information form created by the researchers and the Healthcare Professionals' Knowledge of Childhood Autism Questionnaire-Turkish Version were filled by the participants. Correct response rates were divided into quantile values and under 3rd Quartile (Q3) was considered “insufficient information.” Logistic regression analysis was used for descriptive data and factors affecting the level of knowledge. Results: Of 145 participants, 59.3% completed Child and Adolescent Psychiatry (CAP) internship during their medical education, 60.7% completed CAP rotation, and 49.7% attended training or meetings related to autism. The highest rate of correct answers in autism knowledge evaluation questionnaire was on "Information on social interaction," while the lowest rate of correct answers was on "Information on neurodevelopmental diseases." For the total correct answer rates, the Q3 and Q1 values were 68.1% and 89.5%, respectively. According to the logistic regression model, being single (Mean: 3.60) and not having an MCS rotation (Mean: 8.49) predicted a score below Q3. Conclusions: Our research shows that in this disorder, where early diagnosis is of foremost importance, pediatricians who regularly monitor children have a high level of knowledge about recognizing autistic symptoms; however, there are some deficiencies in answering questions that will resolve the concerns of families about autism. For this reason, pediatricians who examine a child at least 3-4 times a year need pediatric psychiatry training that will give them specific skills in diagnosing and making recommendations, as well as initiating interventions. Another option is being more involved in departments that will enable them to gain experience in autism.

A Stochastic Approximation Method for Simulation-Based Quantile Optimization

We present a gradient-based algorithm for solving a class of simulation optimization problems in which the objective function is the quantile of a simulation output random variable. In contrast with existing quantile (quantile derivative) estimation techniques, which aim to eliminate the estimator bias by gradually increasing the simulation sample size, our algorithm incorporates a novel recursive procedure that only requires a single simulation sample at each step to simultaneously obtain quantile and quantile derivative estimators that are asymptotically unbiased. We show that these estimators, when coupled with the standard gradient descent method, lead to a multi-time-scale stochastic approximation type of algorithm that converges to an optimal quantile value with probability one. In our numerical experiments, the proposed algorithm is applied to optimal investment portfolio problems, resulting in new solutions that complement those obtained under the classical Markowitz mean-variance framework.

ABD'de Cinayet Sayısını Etkileyen Faktörlerin Kantil Regresyon ile İncelenmesi

This study has conducted in the United States of America (USA) in the 1960s, 1970s, 1980s, and 1990s to determine whether divorce rates, unemployment rates, and the population had an impact on homicide numbers. Initially, all variables were examined and interpreted geographically on the map by districts. Subsequently, the stationary circumstance of variables has been tested with the Augmented Dickey-Fuller (ADF) Test, which is one of the unit root tests. After it has found that the variables did not need to stabilize, regression analysis has performed by the Least Squares (LS) method. Quantile regression, which is an alternative method to the LS method, has been used since all the resulting models do not have a normal distribution. These models have been created with 3 diverse quantile values for each period. Among these models, the ones with the highest correlation coefficient are the models having the 0.75 quantile value. Therefore, the results have been obtained from models with the 0.75 quantile value. Hence, for the homicide counts in the USA, those have found that the country population had a positive effect in the 1960s, the country population and the divorce rates had positive effects in the 1970s, the country population had a positive effect in the 1980s, and the country population and the unemployment rates had positive effects in the 1990s. Furthermore, the unemployment rates in the 1970s and 1980s had a negative effect on the homicide counts in the USA.

Robust Optimization of Best-worst Multi-criteria Decision-making Method

The Best-worst multi-criteria decision-making method can determine optimal weight value of each criteria. It uses two vectors for pairwise comparisons in multi-criteria decision-making problem. This paper improves the original method from the perspective of robust optimization. Four robust counterpart constraints instead of two linear constraints in original optimization model are proposed. The decision-making problem can divide into full consistent and non-full consistent problems by classifying parameter value. We can achieve a unique set of interval solution in full consistent decision-making problem. Non-full consistent problem can result in multiple sets of optimal interval solution. The result which we get from this method is more effective than the original method. Each criterion can achieve optimal weight interval value. Then we take quantile value of each interval as optimal weight. This is effectively illustrated in the numerical test at the end of the paper.

Quantile-based versus Sobol sensitivity analysis in limit state design

In limit state design, the reliability of building constructions is generally verified using design quantiles. The design resistance of a structure is explicitly expressed as a low quantile of the cumulative distribution function of resistance. The aim of this article is to show the connections and differences between quantile-oriented sensitivity analysis subordinated to a contrast and classic Sobol sensitivity analysis. Changing the fixed input variable causes synchronous change in the quantile and mean value, but how do the results of these two sensitivity analyses differ? The question is whether or not the changes around the design quantile (measured by contrast indices) are similar to the changes around the mean value, which are measured using Sobol’s indices. Comparison is performed on a case study, where the resistance of the structure is expressed by a non-linear function, the inputs of which are random material and geometric characteristics of the structure. The non-dimensional slenderness is a deterministic parameter, which changes the influence of input variables on the resistance as the model output. It was concluded upon comparing the results of both sensitivity analyses that the rank of the most important variables is the same for both low and high slenderness and is similar for intermediate slenderness. However, the interaction effects are very different. The identification of insignificant variables is the same. Other significant similarities and differences between both types of sensitivity analyses are presented in the article.

Spatial identification and temporal prediction of air pollution sources using conditional bivariate probability function and time series signature.

Accurate identification of distant, large, and frequent sources of emission in cities is a complex procedure due to the presence of large-sized pollutants and the existence of many land use types. This study aims to simplify and optimize the visualization mechanism of long time-series of air pollution data, particularly for urban areas, which is naturally correlated in time and spatially complicated to analyze. Also, we elaborate different sources of pollution that were hitherto undetectable using ordinary plot models by leveraging recent advances in ensemble statistical approaches. The high performing conditional bivariate probability function (CBPF) and time-series signature were integrated within the R programming environment to facilitate the study's analysis. Hourly air pollution data for the period between 2007 to 2016 is collected using four air quality stations, (ca0016, ca0058, ca0054, and ca0025), situated in highly urbanized locations that are characterized by complex land use and high pollution emitting activities. A conditional bivariate probability function (CBPF) was used to analyze the data, utilizing pollutant concentration values such as Sulfur dioxide (SO2), Nitrogen oxides (NO2), Carbon monoxide (CO) and Particulate Matter (PM10) as a third variable plotted on the radial axis, with wind direction and wind speed variables. Generalized linear model (GLM) and sensitivity analysis are applied to verify and visualize the relationship between Air Pollution Index (API) of PM10 and other significant pollutants of GML outputs based on quantile values. To address potential future challenges, we forecast 3 months PM10 values using a Time Series Signature statistical algorithm with time functions and validated the outcome in the 4 stations. Analysis of results reveals that sources emitting PM10 have similar activities producing other pollutants (SO2, CO, and NO2). Therefore, these pollutants can be detected by cross selection between the pollution sources in the affected city. The directional results of CBPF plot indicate that ca0058 and ca0054 enable easier detection of pollutants' sources in comparison to ca0016 and ca0025 due to being located on the edge of industrial areas. This study's CBPF technique and time series signature analysis' outcomes are promising, successfully elaborating different sources of pollution that were hitherto undetectable using ordinary plot models and thus contribute to existing air quality assessment and enhancement mechanisms.

Sex-specific reference values and determinants of infra-renal abdominal aortic diameter among women and men from general population

Abstract Introduction Abdominal aortic diameter has shown to be a marker of adverse cardiovascular outcomes. Among the non-aneurysmal populations, studies regarding abdominal aortic diameter normal reference values are sparse. Moreover, data regarding the associations between cardiovascular risk factors and aortic diameter among men and women are limited. Purpose To establish age- and sex-specific distribution of the infra-renal abdominal aortic diameters among non-aneurysmal older adults from the general population and to investigate the associations between cardiovascular risk factors and aortic diameters in men and women. Methods From a population-based cohort, 4032 participants (mean age, 67.2 years; 60.4% women) with infra-renal diameter assessment and without history of cardiovascular disease were included. Mean and quantile values of diameters were calculated in different age groups. Multiple linear regression analysis was used to detect the association of cardiovascular risk factors with diameters in men and women. Results The mean crude diameter was larger in men [mean (SD): 19.5 (2.6) mm] compared to women [17.0 (2.4)mm] but after adjustment for body surface area (BSA), the differences were small. There was a non-linear relationship between age and diameter (p<0.001). After 66 years of age, the increase in diameter with increasing age was attenuated. After age 74 years in women and 71 years in men, the relationship between age and infra-renal aortic diameter was no longer statistically significant (Figure). Waist [standardized β (95% CI): 0.02 (0.0–0.04) in women and 0.03 (0.01–0.06) in men] and diastolic blood pressure [0.04 (0.02–0.05) in women and 0.02 (0.0–0.04) in men] were the risk factors for diameters in both sexes. Body mass index [0.02 (0.01–0.09)], systolic blood pressure [−0.01 (−0.02 to −0.01)], smoking status [0.21 (0.02–0.39)], cholesterol [−0.19 (−0.29 to −0.09)], and lipid-lowering medication [−0.47 (−0.71 to −0.23)] were significantly associated with aortic diameter only in women. Conclusion The differences in the crude abdominal aortic diameter between women and men diminished after taking into account the BSA. The abdominal aortic diameter increased steeply with advancing age and up to 66 years of age. However, after 74 years in women and 71 years in men, the diameter values reached a plateau. We also observed sex differences in the associations of cardiovascular risk factors with abdominal aortic diameter. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Netherlands Organization for the Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly (RIDE)

Scenario Grouping and Decomposition Algorithms for Chance-Constrained Programs

A lower bound for a finite-scenario-based chance-constrained program is the quantile value corresponding to the sorted optimal objective values of scenario subproblems. This quantile bound can be improved by grouping subsets of scenarios at the expense of solving larger subproblems. The quality of the bound depends on how the scenarios are grouped. In this paper, we formulate a mixed-integer bilevel program that optimally groups scenarios to tighten the quantile bounds. For general chance-constrained programs, we propose a branch-and-cut algorithm to optimize the bilevel program, and for chance-constrained linear programs, a mixed-integer linear-programming reformulation is derived. We also propose several heuristics for grouping similar or dissimilar scenarios. Our computational results demonstrate that optimal grouping bounds are much tighter than heuristic bounds, resulting in smaller root-node gaps and better performance of scenario decomposition for solving chance-constrained 0-1 programs. Also, the optimal grouping bounds can be greatly strengthened using larger group size. Summary of Contribution: Chance-constrained programs are in general NP-hard but widely used in practice for lowering the risk of undesirable outcomes during decision making under uncertainty. Assuming finite scenarios of uncertain parameter, chance-constrained programs can be reformulated as mixed-integer linear programs with binary variables representing whether or not the constraints are satisfied in corresponding scenarios. A useful quantile bound for solving chance-constrained programs can be improved by grouping subsets of scenarios at the expense of solving larger subproblems. In this paper, we develop algorithms for optimally and heuristically grouping scenarios to tighten the quantile bounds. We aim to improve both the computation and solution quality of a variety of chance-constrained programs formulated for different Operations Research problems.

An adaptive soft sensor method of D-vine copula quantile regression for complex chemical processes

Non-linear and non-Gaussian properties are challenging topics in the soft sensor modeling of chemical processes, and fluctuations in the environmental conditions of chemical plants will also affect the accuracy of soft sensor models. This paper proposes an adaptive soft sensor method of D-vine copula quantile regression (aDVQR). In the modeling process, a sparse vine model is established using the Bayesian information criterion. Then, the conditional quantile function value of the specified quantile can be obtained via the recursive nesting method by the h function. An online model updating system based on the aDVQR model is also proposed, and an adaptive soft sensor model is established. The proposed adaptive soft sensor method can successfully approximate the non-linear and non-Gaussian relationships between variables and adapt to unstable environments. Finally, a numerical example and an example of the ethylene industry are used to verify the effectiveness of the proposed method.

Herding in Fast Moving Consumer Group Sector: Equity Market Asymmetry and Crisis

This study empirically examines herd behavior for fast moving consumer goods (FMCG) sector stocks under varied market return conditions and the period during the global financial crisis and its aftermath. We examine the sample of stocks trading on the Nifty FMCG Index of the Indian equity market from January 2008 up to December 2018 using the dispersion measure of cross sectional absolute deviation and examine its relationship with the market return to explore herd phenomenon. Quantile regression estimate is used and the results of the study validate rational asset pricing models as the sector does not display herding. In contrast, anti-herd behavior at lower and median quantile values is observed. A possible reason can be the non-cyclical nature of the industry where investors rely more on the fundamentals rather than crowd chasing. We also findthe absence of herd phenomenon during the market asymmetries of bull and bear phases, extreme movements, the period of the global financial crisis, and afterward. We further examine herding under the impact of the information technology (IT) industry and conclude that significant return movements in IT sector impact dispersions in the FMCG industry. Also, there is a co-varying risk between the two sectors confirming the spillover in an integrated market.

The moderating role of renewable and non-renewable energy in environment-income nexus for ASEAN countries: Evidence from Method of Moments Quantile Regression

A vast body of studies estimates the impact of energy consumption on the environment. A typical empirical study either use aggregate energy consumption or apply conventional econometric techniques in modelling the nexus of energy, income and environment. To correct these gaps, the objective of the study is to use renewable and non-renewable energy consumption in analyzing energy-income-environment nexus, and to apply the novel Method of Moments Quantile Regression for ASEAN countries. The outcomes indicate that non-renewable energy consumption stimulate carbon emissions across all quantiles (10th to 90th), the value of the 10th quantile is 0.257 which rises to 0.501 till 90th quantile. Whereas, the renewable energy consumption leads to a decrease in CO2 emissions across all the quantiles (10th to 90th) but this association is statistically insignificant at higher quantiles from 60th to 90th. The empirical outcomes also verify the presence of the environmental Kuznets curve relationship, which is statistically significant from the middle (30th) to higher (90th) quantiles. Moreover, the finding of panel estimation approaches (FMOLS, DOLS, FE-OLS) also verify the existence of the EKC hypothesis in ASEAN countries. Their finding also describes that 1% increase in non-renewable energy consumption increase CO2 emission by 0.29%, 0.26% and 0.30% whereas 1% increase in the usage of renewable energy reduces CO2 emission by 0.17%, 0.15% and 0.17% in case of FMOLS, DOLS and FE-OLS respectively. The empirical results conclude that the government should encourage and subsidize the sources of green energy to tackle environmental degradation. More policy implications are further discussed in the study.

Effect of strip shelterwood-cuts on the crown morphology plasticity of natural regenerated Pinus tabuliformis saplings in northeastern China.

The study analysed the effect of shelterwood-cut strips on the outermost crown profiles and crown characteristics of naturally regenerated Pinus tabuliformis saplings in northeastern China. A total of 49 regenerated saplings from shelterwood-cut strips and 30 from uncut strips were collected. Nonlinear quantile regression was used to develop the outermost crown profile model for the saplings from the shelterwood-cut and uncut strips. The quantile value suitable for describing the outermost crown profiles of the two types of strips was selected using nonparametric boundary regression. The difference in crown morphologies between the shelterwood-cut strips and uncut strips was compared. The results showed that with the same diameter at breast height, the crown radii of the uncut strip saplings were larger than those of the shelterwood-cut strip saplings within the range of 0.2–1.0 for the relative depth into the crown. The largest crown radius of the saplings from the uncut strips was larger than that of the saplings from the shelterwood-cut strips. The inflection points of the shelterwood-cut strip sapling crowns were larger than those of the uncut strip sapling crowns. The crown volume of the small uncut strip saplings was larger than that of the shelterwood-cut strip saplings, and the difference in crown volume decreased with increasing sapling size. The saplings in the early stage of the uncut strips showed a greater growth rate than those of the shelterwood-cut strips, but their growth rate slowed over the long term according to branch-length annual growth. The present study provides a reference for forest management strategy decision making in promoting natural regeneration.

Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models

AbstractQuantile is an important quantity in reliability analysis, as it is related to the resistance level for defining failure events. This study develops a computationally efficient sampling method for estimating extreme quantiles using stochastic black box computer models. Importance sampling has been widely employed as a powerful variance reduction technique to reduce estimation uncertainty and improve computational efficiency in many reliability studies. However, when applied to quantile estimation, importance sampling faces challenges, because a good choice of the importance sampling density relies on information about the unknown quantile. We propose an adaptive method that refines the importance sampling density parameter toward the unknown target quantile value along the iterations. The proposed adaptive scheme allows us to use the simulation outcomes obtained in previous iterations for steering the simulation process to focus on important input areas. We prove some convergence properties of the proposed method and show that our approach can achieve variance reduction over crude Monte Carlo sampling. We demonstrate its estimation efficiency through numerical examples and wind turbine case study.