Nonparametric Estimation Research Articles

Nonparametric richness estimators Chao1 and ACE must not be used with amplicon sequence variant data.

Microbial ecologists use alpha diversity metrics for estimating species richness and evenness from data obtained by high-throughput sequencing of small subunit ribosomal RNA genes. This perspective argues that the nonparametric richness estimators Chao1 and ACE should never be used with ASV data because the default process of generating amplicon sequence variants (ASVs) removes singletons, which are specifically required for these estimate calculations. In addition, retaining singletons, if/when possible, will contribute a large proportion of artifacts to this abundance category, leading to extreme richness overestimation. We recommend the use of alternative sequence clustering strategies and/or diversity metrics to avoid generating meaningless richness estimates from ASV data.

Behavioural factors matter for the adoption of climate-smart agriculture

Increasing agricultural productivity while ensuring environmental sustainability are two important targets in achieving the sustainable development goals under climatic shocks. In this regard, different climate-smart agricultural (CSA) practices have been recommended and promoted to meet these goals and targets. However, the adoption of these practices remains low and variable. For the most part, low adoption has been attributed to external factors. Behavioural and psychological factors also matter but have received little empirical and policy attention. In this study, we examine the relationship between aspirations, aspiration gaps, and the adoption of CSA practices such as crop rotation, intercropping, fallowing, and organic soil amendments. Employing parametric and non-parametric estimation techniques on a pooled farm household survey from Cameroon and Kenya, we show that aspirations are associated with the use of crop rotation and organic soil amendments. We also investigate the theorized non-monotonic inverse U-shaped relationship between aspiration gaps and investments. We find evidence of this relationship for the adoption of these CSA practices, suggesting an aspiration failure for smallholder farmers. These results imply that aspirations that are ahead but not too far ahead of the current state serve as the best incentives for stimulating the adoption of CSA practices. Employing the multivariate probit model, we further highlight interdependencies in the use of these CSA practices. Specifically, we underscore significant complementarities, suggesting the bundled use of these practices. Overall, the analysis demonstrates that aspirations matter for farmer decision-making with many implications for agricultural, food, and environmental policies.

Correlation dimension and entropy in the assessment of sex differences based on human gait data.

It is proved that there are differences between gait performed by females and males, which appear in movements of selected body parts. Despite numerous state-of-the-art studies related to the discriminative analysis of motion capture data, the question of whether measures of signal complexity and uncertainty can extract valuable features for the problem of sex distinction still remains open. It is the subject of the paper. Correlation dimension, as well as approximate and sample entropies, are selected to describe motion data. In the numerical experiments, the collected dataset with 884 samples of 25 females and 30 males was used. The measurements took place in the Human Motion Laboratory (HML), equipped with a highly precise motion capture system. Two variants of data representation were investigated-time series that contain joint rotations of taken skeleton model as well as positions of the markers attached to the human body. Finally, a comparative analysis between the populations of females and males using descriptive statistics, non-parametric estimation, and statistical hypotheses verification was carried out. There are statistically significant sex differences extracted by the taken measures. In general, the movements of lower limbs result in greater values of correlation dimension and entropies for females, while selected upper body parts play a similar role for males. The dissimilarities are mainly observed in hip, ankle, shoulder, and head movements. Correlation dimension and entropy measures provide robust and explainable features of motion capture data with a valuable description of the human locomotion system. Thus, beyond the importance of discovered differences between females and males, their interpretation and understanding are also known.

Simulation and evaluation study of atmospheric aerosol nonsphericity as a function of particle size

Aerosol nonsphericity causes great uncertainty in radiative forcing assessments and climate simulations. Although considerable studies have attempted to quantify this uncertainty, the relationship between aerosol nonsphericity and particle size is usually not considered, thus reducing the accuracy of the results. In this study, a coupled inversion algorithm combining an improved stochastic particle swarm optimization algorithm and angular light scattering is used for the nonparametric estimation of aerosol nonsphericity variation with particle size, and the optimal sample selection method is employed to screen the data. Based on the verification of inversion accuracy, the variation of aerosol aspect ratio with particle size based on the ellipsoidal model in global regions has been obtained from Aerosol Robotic Network (AERONET) data, and the effect of nonsphericity on radiative forcing and dry deposition has been studied. The results show that the aspect ratio increases with particle size in all regions, with the maximum ranging from 1.4 to 1.8 in the desert, reflecting the differences in aerosol composition at different particle sizes. In radiation calculations, considering aerosol nonsphericity makes the aerosol cooling effect weaker and surface radiative fluxes increase, but hardly changes the aerosol absorption, with maximum differences of 9.22% and 22.12% at the bottom and top of the atmosphere, respectively. Meanwhile, the differences in radiative forcing between aspect ratios as a function of particle size and not varying with particle size are not significant, averaging less than 2%. Besides, the aspect ratio not varying with particle size underestimates the deposition velocity of small particles and overestimates that of large particles compared to that as a function of particle size, with maximum differences of 7% and 4%, respectively.

Electric Vehicle and Photovoltaic Power Scenario Generation under Extreme High-Temperature Weather

In recent years, with the intensification of global warming, extreme weather has become more frequent, intensifying the uncertainty of new energy output and load power, and seriously affecting the safe operation of power systems. Scene generation is an effective method to solve the uncertainty problem of stochastic planning of integrated systems of new energy generation. Therefore, this paper proposes a scenario generation and scenario reduction model of photovoltaic (PV) output and electric vehicle (EV) load power under extreme weather based on the copula function. Firstly, the non-parametric kernel density estimation method is used to fit a large number of sample data. The kernel density estimation expressions of PV and EV powers under extreme weather conditions are obtained and the corresponding goodness of fit tests are carried out. Then, a variety of joint distribution models based on the copula function are established to judge the goodness of fit of each model, and the optimal copula function is selected as the joint probability distribution function by combining the Kendall and Spearman correlation coefficients of each model. Finally, the optimal copula joint probability distribution is used to generate PV and EV power scenarios. The data of extremely hot weather in a certain province were selected for an example analysis. The results show that the output scenario obtained conforms to the correlation under this extreme weather, and has higher accuracy in reflecting the actual PV output and load power in this province under this extreme weather, which can provide a reference for reliability analyses of power systems and power grid planning.

Bernstein-based estimation of the cross ratio function

Local association measures provide useful insights in time-varying changes in association, especially between time-to-event variables. Such local dependence between two correlated random variables can be measured using the cross ratio function. The cross ratio function is defined as the ratio of conditional hazard functions which have been estimated using Bernstein polynomials before. Alternatively, the cross ratio function can be expressed in terms of (derivatives of) the joint survival function of the two random variables. In this paper, we discuss an alternative Bernstein-based plug-in estimator of the cross ratio function in which each of the ingredients is estimated separately. Next to asymptotic normality of the nonparametric estimator, a simulation study is used to assess its finite-sample performance. Finally, the novel estimator is applied to a real-life data application.

Intermediate service input distortions and total factor productivity: Evidence from China.

China has the most minimal proportion of intermediate service inputs among the input-output datasets encompassing 43 countries and regions within the WIOD database. This study employs a non-parametric estimation methodology to compute the appropriate input levels for two distinct categories of intermediate goods. Furthermore, it evaluates the decline in total factor productivity resulting from distortions in intermediate input. The research findings are as follows: 1) China's producer services exhibit an output elasticity approximately twice that of industrial intermediate goods. However, the input for producer services is only about half of that for the latter. This points to a notable deviation of China's input for producer services from the optimal level. 2) Upon achieving an optimal level of input allocation for intermediates, the entire industry could experience an 11.48% boost in total factor productivity. In particular, the manufacturing sector could witness an impressive surge of 33.91%. 3) A positive correlation is discerned between intermediate input distortions and the import of intermediate products.

Propensity Score Regression for Causal Inference With Treatment Heterogeneity

Understanding how treatment effects vary on individual characteristics is critical in the contexts of personalized medicine, personalized advertising and policy design. When the characteristics are of practical interest are only a subset of full covariate, non-parametric estimation is often desirable; but few methods are available due to the computational difficult. Existing non-parametric methods such as the inverse probability weighting methods have limitations that hinder their use in many practical settings where the values of propensity scores are close to 0 or 1. We propose the propensity score regression (PSR) that allows the non-parametric estimation of the heterogeneous treatment effects in a wide context. PSR includes two non-parametric regressions in turn, where it first regresses on the propensity scores together with the characteristics of interest, to obtain an intermediate estimate; and then, regress the intermediate estimates on the characteristics of interest only. By including propensity scores as regressors in the non-parametric manner, PSR is capable of substantially easing the computational difficulty while remain (locally) insensitive to any value of propensity scores. We present several appealing properties of PSR, including the consistency and asymptotical normality, and in particular the existence of an explicit variance estimator, from which the analytical behaviour of PSR and its precision can be assessed. Simulation studies indicate that PSR outperform existing methods in varying settings with extreme values of propensity scores. We apply our method to the national 2009 flu survey (NHFS) data to investigate the effects of seasonal influenza vaccination and having paid sick leave across different age groups.

Nonparametric Estimation and Testing for Panel Count Data with Informative Terminal Event

Nonparametric Estimation and Testing for Panel Count Data with Informative Terminal Event

An Improved Internet of Thing-based Optimized SVM Approach for ECG-founded Cardiac Arrhythmia Classification

Cardiovascular diseases (CVD) stand as the leading cause of global mortality, claiming millions of lives annually. An electrocardiogram (ECG) records the heart's electrical activity based on the Internet of Things (IoT), crucial in detecting cardiac arrhythmias (CA), characterized by irregular heart rates and rhythms. Signals from the MIT-BIH Arrhythmia Physio net database are analyzed. This chapter aims to propose a hybrid approach merging Genetic Algorithm-Support Vector Machine (GSVM) and Particle Swarm Optimization-Support Vector Machine (PSVM) for CA classification. The study introduces an algorithm for categorizing ECG beats into six groups using Independent Component Analysis (ICA)-derived features. Optimal SVM settings are determined using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) on ICA features computed via non-parametric power spectral estimation. The research delves into the origins and methodologies of GA and PSO. Simulation results comparing GSVM and PSVM are presented, emphasizing PSVM's superior performance in accuracy, sensitivity, specificity, and positive predictivity. Detailed performance metrics, including Sensitivity, Specificity, Positive Predictivity, and Accuracy percentages, are scrutinized and compared against the top classifier. The findings endorse PSVM's superiority over GSVM, indicating enhanced performance across multiple evaluation criteria.

An Effective Internet of Things based Assessment of ANN and ANFIS algorithms for Cardiac Arrhythmia

Reducing the influence of significant noise signal components on the obtained raw ECG signal is essential for precise identification of cardiac arrhythmias (CA), which frequently present as irregularities in heart rate or rhythm. Preprocessing is used to remove noise signals and baseline drift from the ECG wave that is recorded using the internet of things (IoT). After that, the denoised signal is subjected to dimensionality reduction and feature extraction. In order to determine whether classification method is more effective in detecting cardiac arrhythmias, this study compares two methods: an adaptive neuro-fuzzy inference system and artificial feed-forward neural networks trained with the back-propagation learning algorithm. An Adaptive Neuro Fuzzy Inference System analyses ICA features obtained by non-parametric power spectral estimates, and an Artificial Neural Network (ANN) classifier uses the ECG signal's morphological and statistical aspects to identify patterns. The creation of artificial feed-forward neural networks provides a rich framework for studying the Back Propagation Algorithm. Sensitivity, specificity, accuracy, and positive predictiveivity are some of the performance characteristics that are thoroughly examined. An overall accuracy of 97.79%, sensitivity of 99.82%, specificity of 99.68%, and positive predictivity of 98.58% were seen in the results of the Artificial Neural Feed Forward Network (ANFFN). The Adaptive Neuro Fuzzy Inference System (ANFIS) outperforms these metrics with an astounding overall accuracy of 99.62%, specificity of 98.63%, and positive predictivity of 99.46%. With a classification accuracy of 99.82%, ANFIS demonstrates to be the most effective classifier for identifying cardiac arrhythmias.

Application of the evolutionary approach to structural and parametric identification of dynamic objects

The paper examines the evolutionary approach to structural and parametric identification of dynamic systems in the form of differential equations. The approach is based on a genetic programming algorithm to determine a structure of the equation and differential evolution method for parameters selection. The author proposed approach based on such evolutionary algorithms as genetic programming and differential evolution. The search for the structure is carried out by genetic programming. The selection of numerical parameters and initial conditions is implemented by a method of differential evolution. The problem of finding a model that describes changes in the efficiency of a hydraulic system is solved with the help of this approach. The proposed approach is compared with a recurrent neural network and a nonparametric kernel regression estimation.

Latent Class Proportional Hazards Regression with Heterogeneous Survival Data.

Heterogeneous survival data are commonly present in chronic disease studies. Delineating meaningful disease subtypes directly linked to a survival outcome can generate useful scientific implications. In this work, we develop a latent class proportional hazards (PH) regression framework to address such an interest. We propose mixture proportional hazards modeling, which flexibly accommodates class-specific covariate effects while allowing for the baseline hazard function to vary across latent classes. Adapting the strategy of nonparametric maximum likelihood estimation, we derive an Expectation-Maximization (E-M) algorithm to estimate the proposed model. We establish the theoretical properties of the resulting estimators. Extensive simulation studies are conducted, demonstrating satisfactory finite-sample performance of the proposed method as well as the predictive benefit from accounting for the heterogeneity across latent classes. We further illustrate the practical utility of the proposed method through an application to a mild cognitive impairment (MCI) cohort in the Uniform Data Set.

Lévy Adaptive B-spline Regression via Overcomplete Systems

The estimation of functions with varying degrees of smoothness is a challenging problem in the nonparametric function estimation. In this paper, we propose the LABS (L\'{e}vy Adaptive B-Spline regression) model, an extension of the LARK models, for the estimation of functions with varying degrees of smoothness. LABS model is a LARK with B-spline bases as generating kernels. The B-spline basis consists of piecewise k degree polynomials with k-1 continuous derivatives and can express systematically functions with varying degrees of smoothness. By changing the orders of the B-spline basis, LABS can systematically adapt the smoothness of functions, i.e., jump discontinuities, sharp peaks, etc. Results of simulation studies and real data examples support that this model catches not only smooth areas but also jumps and sharp peaks of functions. The proposed model also has the best performance in almost all examples. Finally, we provide theoretical results that the mean function for the LABS model belongs to the certain Besov spaces based on the orders of the B-spline basis and that the prior of the model has the full support on the Besov spaces.

Nonparametric path function estimation of Fourier series at low oscillations for modelling timely paying credit

Nonparametric path function estimation of Fourier series at low oscillations for modelling timely paying credit

An Improvement of the Nonparametric Frequency Estimation Below Two Signal Periods Using Two Windows

An Improvement of the Nonparametric Frequency Estimation Below Two Signal Periods Using Two Windows

Nonparametric path function estimation of Fourier series at low oscillations for modelling timely paying credit

Nonparametric path function estimation of Fourier series at low oscillations for modelling timely paying credit

Dynamic Effective Connectivity Learning Based on Nonparametric State Estimation and GAN

Dynamic Effective Connectivity Learning Based on Nonparametric State Estimation and GAN

Weighted extropy measures in general Morgenstern family under k-record values with application to medical data

In this paper, we study the marginal distribution of concomitants of k-record (KR) values from generalized Farlie–Gumbel–Morgenstern (GFGM) of bivariate distributions. In addition, the joint distribution of concomitants of KR for this family is obtained. Furthermore, some useful recurrence relations between moments of concomitants are derived. In addition, the hazard rate, the reversed hazard rate, and mean residual life functions of concomitants for this family are obtained. Some recent new measures of information, such as weighted extropy, weighted cumulative past extropy, and weighted cumulative residual extropy, are investigated for the concomitant of KR under the GFGM family. A non-parametric estimator of the proposed measure is provided by combining the empirical method with the concurrent use of KR in the GFGM family. Finally, we analyzed real-world data to examine our findings.

Asymptotic Normality of Single Functional Index Quantile Regression for Functional Data with Missing Data at Random

This work addresses the problem of the nonparametric estimation of the regression function, namely the conditional distribution and the conditional quantile in the single functional index model (SFIM) under the independent and identically distributed condition with randomly missing data. The main result of this study was the establishment of the asymptotic properties of the estimator, such as the almost complete convergence rates. Moreover, the asymptotic normality of the constructs was obtained under certain mild conditions. Lastly, the authors discussed how to apply the result to construct confidence intervals.