Semiparametric Methods Research Articles

Being innovative is not enough: a semi-parametric assessment of the efficiency of post-incubation innovative firms

PurposeThis paper aims to assess whether incubation affects the technical efficiency of innovative firms after entering the market. The study of efficiency allows firms to understand how well resources have been used in production processes. The research intends to contribute to the literature on the performance of incubated firms.Design/methodology/approachThis study estimates the relative efficiency of innovative firms adopting a DEA-based two-stage semi-parametric method. Incubation, firm age and initial capital are used for explaining the relative performance of previously incubated firms compared to non-incubated ones over a six-year period of activity. This research focuses on Italian innovative firms using a large sample of companies.FindingsResults show that incubators have a positive and significant effect on efficiency for firms that have been in the market for more than two years. Efficiency also improves with age and with the level of initial capital of the firm.Research limitations/implicationsThis analysis is limited to the quantitative dimension of inputs as reported in the balance sheets, without qualitative considerations.Practical implicationsFindings enhance firms' understanding of the role of incubators as neutral places to develop a business culture of efficiency. From an empirical standpoint, this study provides useful insights to start-uppers who intend to attend incubation programs. Overall, incubators matter to the extent that they enable new firms, net of those that fail to survive in the first two years of activity, to improve their efficiency in the use of inputs. This research also suggests incubators consider the start-ups’ potential of being efficient.Social implicationsFindings provide tips to policymakers when they are called upon to propose funding programs to support prominent firms entering the business scalability.Originality/valueThis study contributes to the literature on the relative performance of post-incubated firms, highlighting the efficiency frontier analysis. This methodological approach is relatively new in this field. It allows researchers to study the innovative firms' performance in relative terms, that is with respect to the input level. It integrates the performance-based with efficiency frontier analysis. Also, this study reinforces the idea that incubators prepare start-ups to develop capacities and managerial skills, which will be useful in post-incubation life to improve their cost competitiveness.

Loss aversion and the welfare ranking of policy interventions

This paper develops theoretical criteria and econometric methods to rank policy interventions in terms of welfare when individuals are loss-averse. Our new criterion for “loss aversion-sensitive dominance” defines a weak partial ordering of the distributions of policy-induced gains and losses. It applies to the class of welfare functions which model individual preferences with non-decreasing and loss-averse attitudes towards changes in outcomes. We also develop new semiparametric statistical methods to test loss aversion-sensitive dominance in practice, using nonparametric plug-in estimates; these allow inference to be conducted through a special resampling procedure. Since point-identification of the distribution of policy-induced gains and losses may require strong assumptions, we extend our comparison criteria, test statistics, and resampling procedures to the partially-identified case. We illustrate our methods with a simple empirical application to the welfare comparison of alternative income support programs in the US.

Penalised semi-parametric copula method for semi-competing risks data: application to hip fracture in elderly

Abstract Hip fracture is a severe complication in the elderly. The affected people are at a higher risk of second fracture and death occurrence, and the best treatment for hip fractures is still being debated. Aside from the treatment, many factors, such as comorbidity conditions, may be associated with second fracture and death occurrence. This study aims to identify effective treatments and important covariates and estimate their effects on the progression of second fracture and death occurrence in hip fracture elderly patients using the semi-competing risks framework, because death dependently censors a second fracture but not vice versa. Due to the complex semi-competing risks data, performing variable selection simultaneously for second fracture and death occurrence is difficult. We propose a penalised semi-parametric copula method for semi-competing risks data. Specifically, we use separate Cox semi-parametric models for both margins and employ a copula to model the two margins’ dependence. We develop a coordinate-wise optimisation algorithm that takes into account the data structure and copula function’s complexities. Simulations show that the proposed method outperforms the traditional penalised marginal method. We apply the proposed method to a population-based cohort study of hip fracture elderly patients, providing new insights into their treatment and clinical management.

Asymptotic Relative Efficiency of Parametric and Nonparametric Survival Estimators

The dominance of non- and semi-parametric methods in survival analysis is not without criticism. Several studies have highlighted the decrease in efficiency compared to parametric methods. We revisit the problem of Asymptotic Relative Efficiency (ARE) of the Kaplan–Meier survival estimator compared to parametric survival estimators. We begin by generalizing Miller’s approach and presenting a formula that enables the estimation (numerical or exact) of ARE for various survival distributions and types of censoring. We examine the effect of follow-up time and censoring on ARE. The article concludes with a discussion about the reasons behind the lower and time-dependent ARE of the Kaplan–Meier survival estimator.

Semiparametric estimation of long-term treatment effects

Long-term outcomes of experimental evaluations are necessarily observed after long delays. We develop semiparametric methods for combining the short-term outcomes of experiments with observational measurements of short-term and long-term outcomes, in order to estimate long-term treatment effects. We characterize semiparametric efficiency bounds for various instances of this problem. These calculations facilitate the construction of several estimators. We analyze the finite-sample performance of these estimators with a simulation calibrated to data from an evaluation of the long-term effects of a poverty alleviation program.

Joint semiparametric kernel network regression.

Variable selection and graphical modeling play essential roles in highly correlated and high-dimensional (HCHD) data analysis. Variable selection methods have been developed under both parametric and nonparametric model settings. However, variable selection for nonadditive, nonparametric regression with high-dimensional variables is challenging due to complications in modeling unknown dependence structures among HCHD variables. Gaussian graphical models are a popular and useful tool for investigating the conditional dependence between variables via estimating sparse precision matrices. For a given class of interest, the estimated precision matrices can be mapped onto networks for visualization. However, the limitation of Gaussian graphical models is that they are only applicable to discretized response variables and for the case when , where is the number of variables and is the sample size. They are necessary to develop a joint method for variable selection and graphical modeling. To the best of our knowledge, the methods for simultaneously selecting variable selection and estimating networks among variables in the semiparametric regression settings are quite limited. Hence, in this paper, we develop a joint semiparametric kernel network regression method to solve this limitation and to provide a connection between them. Our approach is a unified and integrated method that can simultaneously identify important variables and build a network among those variables. We developed our approach under a semiparametric kernel machine regression framework, which can allow for nonlinear or nonadditive associations and complicated interactions among the variables. The advantages of our approach are that it can (1) simultaneously select variables and build a network among HCHD variables under a regression setting; (2) model unknown and complicated interactions among the variables and estimate the network among these variables; (3) allow for any form of semiparametric model, including non-additive, nonparametric model; and (4) provide an interpretable network that considers important variables and a response variable. We demonstrate our approach using a simulation study and real application on genetic pathway-based analysis.

A comparative study between the system reliability evaluation methods: case study of mining dump trucks

The shovel-truck system is a widely used technique for haulage systems in surface mining operations. However, predicting the failure patterns of complex systems requires accurate failure prediction techniques. In this study, several major system reliability evaluation groups, including non-parametric, parametric, and semi-parametric methods, are investigated, and their effectiveness is compared to identify the best group for predicting the failure patterns of complex systems such as mining dump trucks, which operate in harsh environments. A historical dataset of time to failure (TTF) and maintenance data was collected. Then, the system’s reliability was evaluated using the major TTF data analysis methods. The findings demonstrated that all the major system reliability evaluation groups produced similar curves; however, the semi-parametric method outperformed the other methods. This result underscores that this system reliability evaluation group is the most effective method for complex systems. Also, it was found that the dump truck reliability dropped to 50% after 40 operation hours, demonstrating the critical importance of implementing preventive maintenance to enhance the system’s performance and ensure operation safety. In addition, this study provided an appropriate insight into the predictive methods and offered an accurate estimation of the failure pattern of complex systems, resulting in availability and productivity improvement.

Machine Learning Techniques for Analyzing Survival Data of Breast Cancer Patients in Baghdad

The Machine learning methods, which are one of the most important branches of promising artificial intelligence, have great importance in all sciences such as engineering, medical, and also recently involved widely in statistical sciences and its various branches, including analysis of survival, as it can be considered a new branch used to estimate the survival and was parallel with parametric, nonparametric and semi-parametric methods that are widely used to estimate survival in statistical research. In this paper, the estimate of survival based on medical images of patients with breast cancer who receive their treatment in Iraqi hospitals was discussed. Three algorithms for feature extraction were explained: The first principal component algorithm, The second kernel principal component algorithm, and The last is the faster ICA algorithm. Then the important features extracted in the three algorithms for features extraction will be entered into machine learning algorithms: The first K nearest neighbor algorithm, The second survival tree algorithm (or regression tree), and the last random survival forests algorithm.
 Two criteria for comparing the best models to estimate survival have relied on the MSE and the C-Index. The best model for estimating and predicting survival is the use of the fastest ICA algorithm with the random survival forest algorithm that gave the lowest amount to MSE and the highest value to the C-Index. Accordingly, we recommend doctors and medical professionals in Iraq adopt this model to estimate survival for patients with breast cancer.

A nested semiparametric method for case‐control study with missingness

AbstractWe propose a nested semiparametric model to analyze a case‐control study where genuine case status is missing for some individuals. The concept of a noncase is introduced to allow for the imputation of the missing genuine cases. The odds ratio parameter of the genuine cases compared to controls is of interest. The imputation procedure predicts the probability of being a genuine case compared to a noncase semiparametrically in a dimension reduction fashion. This procedure is flexible, and vastly generalizes the existing methods. We establish the root‐ asymptotic normality of the odds ratio parameter estimator. Our method yields stable odds ratio parameter estimation owing to the application of an efficient semiparametric sufficient dimension reduction estimator. We conduct finite sample numerical simulations to illustrate the performance of our approach, and apply it to a dilated cardiomyopathy study.

Heterogeneous interventional effects with multiple mediators: Semiparametric and nonparametric approaches

Abstract We propose semiparametric and nonparametric methods to estimate conditional interventional indirect effects in the setting of two discrete mediators whose causal ordering is unknown. Average interventional indirect effects have been shown to decompose an average treatment effect into a direct effect and interventional indirect effects that quantify effects of hypothetical interventions on mediator distributions. Yet these effects may be heterogeneous across the covariate distribution. We consider the problem of estimating these effects at particular points. We propose an influence function-based estimator of the projection of the conditional effects onto a working model, and show under some conditions that we can achieve root-n consistent and asymptotically normal estimates. Second, we propose a fully nonparametric approach to estimation and show the conditions where this approach can achieve oracle rates of convergence. Finally, we propose a sensitivity analysis that identifies bounds on both the average and conditional effects in the presence of mediator-outcome confounding. We show that the same methods easily extend to allow estimation of these bounds. We conclude by examining heterogeneous effects with respect to the effect of COVID-19 vaccinations on depression during February 2021.

Post-communist firms’ independent green R&D and productivity growth under globalisation

ABSTRACT This article empirically studies the influence of firms’ independent research and development on productivity. It considers two R&D behaviours of firms: independent innovation and dependent imitation. We acquire data from 28 post-communist economies between 2017 and 2019, and innovatively create indices to evaluate how likely firms were to independently perform green R&D that enhance their environmental friendliness. We also estimate firms’ productivity with a semiparametric method. Overall, independent green R&D does not significantly affect productivity. However, further analysis that considers the firms’ globalisation statuses reveals interesting and robust findings. Among firms that are actively involved in the global market, a higher R&D independence does not promote productivity. Firms producing only domestically, however, expect a positive influence of R&D independence on their productivity. This finding remains robust under alternative analyses regarding general R&D. Other factors such as labour input, relationships with the government, and informal market competition also significantly affect productivity.

PEMODELAN PRODUK DOMESTIK BRUTO DI INDONESIA DENGAN PENDEKATAN SEMIPARAMETRIK POLINOMIAL LOKAL DILENGKAPI GUI-R

Gross domestic product is a measuring tool for a country's economy that needs to be known so that the country is able to consider decisions taken regarding future economic policies. The local polynomial semiparametric method that combines parametric regression and local polynomial nonparametric can be one way of predicting a country's GDP. This method is used because in GDP modeling there is one independent variable that has a linear relationship while the other variables have a pattern that tends to cluster. The modeling aims to obtain a semiparametric local polynomial model on GDP in Indonesia with the influence of coal export volume as a parametric independent variable and world oil prices as a nonparametric independent variable from the first quarter of 2005 to the second quarter of 2021 which is equipped with a GUI to simplify calculations. Based on experiments on several types of kernels, bandwidth and model degrees, the best model is local polynomial semiparametric model with Gaussian kernel weighting at degree 2 which has the smallest GCV. This model also has an R-Square value of 89.2% where the value of GDP is strongly influenced by world oil prices and coal export volumes together. The forecasting ability of this best model is said to be good because it has a MAPE of 17.127%.

Semi-parametric estimation of treatment effects in randomised experiments

Abstract We develop new semi-parametric methods for estimating treatment effects. We focus on settings where the outcome distributions may be thick tailed, where treatment effects may be small, where sample sizes are large, and where assignment is completely random. This setting is of particular interest in recent online experimentation. We propose using parametric models for the treatment effects, leading to semi-parametric models for the outcome distributions. We derive the semi-parametric efficiency bound for the treatment effects for this setting, and propose efficient estimators. In the leading case with constant quantile treatment effects, one of the proposed efficient estimators has an interesting interpretation as a weighted average of quantile treatment effects, with the weights proportional to minus the second derivative of the log of the density of the potential outcomes. Our analysis also suggests an extension of Huber’s model and trimmed mean to include asymmetry.

Environmental management, environmental innovation, and productivity growth: a global firm-level investigation

AbstractThis paper empirically investigates the economic effects of environmental activities. To be specific, it investigates the interactive influence of firms' environmental management and environmental innovation on their productivity. We consider both internal and external environmental management practices of global firms observed from 41 countries between 2017 and 2019. We also consider both inputs and outputs of firms' innovation activities that aim to reduce environmental impacts. Multiple indices are constructed to comprehensively evaluate firms' environmental activities, and productivity is estimated with a semi-parametric method. We find that environmental management and environmental innovation are directly correlated to each other and both substantially promote productivity; however, they tend to substitute each other's positive effects on productivity. Other variables such as globalization, government, labor inputs, and informal competition strongly affect firm productivity too.

The impact of venture capital on Chinese SMEs’ sustainable development: a focus on early-stage and professional characteristics

Venture capital is an essential financing option for small and medium-sized enterprises, especially high-tech enterprises. The sustainable development of enterprises is of great significance for promoting the high-quality growth of the economy. In this paper, Chinese-listed companies supported by venture capital from 2002 to 2022 are taken as the research sample. Semiparametric and nonparametric methods are adopted to explore the long-term impact of venture capital on the sustainable development of Chinese enterprises. It is found that the early stage of venture capital companies has a significant promoting effect on the sustainable development of the enterprises. In contrast, professionalism has no significant impact on the sustainable development of enterprises in the short term but has an inhibitory effect in the long term. The results show that venture capital is conducive to enterprises’ standardization and long-term development because it provides funds, supervision, and other noncapital value-added services, such as networking resources, management experience, and market information. Venture capital should refrain from interfering excessively in decision-making in professional and technical fields. This paper addresses the need to research venture capital’s impact on enterprises’ sustainable development. It provides a specific reference for formulating venture capital contracts and related policy recommendations.

Caste, religion and the labor force participation of women: evidence from India

This study examines the origins and evolution of labor force participation rate differences between women from different ethnic groups in India. The data comes from the Indian National Sample Survey covering the period from 1983 to 2012. I use parametric and semi-parametric methods to study inter-group participation gaps and find that the caste gap has been narrowing over time, while the religion gap remains high. I find that Muslim women’s concentration in areas of low economic activity is the main driver of their low LFPR. I also test the role of culture in explaining inter-group gaps, using the India Human Development Survey. There has been a general fall in female participation in the labor market globally in recent years. These methods could advantageously be applied to data from other countries to investigate the employment and livelihood opportunities of women in other multi-ethnic contexts and regions.

Semiparametric Model-Based Adaptive Control for Aortic Pressure Regulation in Ex Situ Heart Perfusion

This paper presents a semi-parametric model-based adaptive control method for the regulation of aortic pressure (AoP) in an <i>ex situ</i> heart perfusion (ESHP) system. The semi-parameter dynamic model of the perfusion system includes a three-element model with a capacitor and two resistors to describe the reference relationship between the aortic pressure and perfusion flow, and a data-driven model to describe the nonlinearity and the uncertainty of the perfusion system. This semi-parametric model gains high accuracy of ESHP model parameters with small size of samples in real time. We integrate the semi-parametric model in an adaptive controller which tunes the control parameters based on the personalized ESHP model to regulate the AoP to maintain the heart&#x0027;s physiological aerobic metabolism. Simulations and experiments (55<inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula>5kg pigs, <inline-formula><tex-math notation="LaTeX">$\boldsymbol{n}$</tex-math></inline-formula>=6) show that the proposed semi-parametric model achieved high accuracy (0.04mmHg) ESHP model personalization in real time (0.55<inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula>0.23s) with a small overshoot less than 2mmHg.

Semi-Parametric Functional Calibration Using Uncertainty Quantification Based Decision Support

Abstract While most calibration methods focus on inferring a set of model parameters that are unknown but assumed to be constant, many models have parameters that have a functional relation with the controllable input variables. Formulating a low-dimensional approximation of these calibration functions allows modelers to use low-fidelity models to explore phenomena at lengths and time scales unattainable with their high-fidelity sources. While functional calibration methods are available for low-dimensional problems (e.g., one to three unknown calibration functions), exploring high-dimensional spaces of unknown calibration functions (e.g., more than ten) is still a challenging task due to its computational cost and the risk for identifiability issues. To address this challenge, we introduce a semiparametric calibration method that uses an approximate Bayesian computation scheme to quantify the uncertainty in the unknown calibration functions and uses this insight to identify what functions can be replaced with low-dimensional approximations. Through a test problem and a coarse-grained model of an epoxy resin, we demonstrate that the introduced method enables the identification of a low-dimensional set of calibration functions with a limited compromise in calibration accuracy. The novelty of the presented method is the ability to synthesize domain knowledge from various sources (i.e., physical experiments, simulation models, and expert insight) to enable high-dimensional functional calibration without the need for prior knowledge on the class of unknown calibration functions.

A spline-assisted semiparametric approach to nonparametric measurement error models

A spline-assisted approach is proposed to handle the measurement error problem in treating the pollution and asthma data. It is well known that the minimax rate of convergence in nonparametric regression function estimation of a random variable measured with error is much slower than the rate in the error free case. A different problem is considered. It is shown that if one is willing to impose a relatively mild assumption in requiring that the error-prone variable has a compact support, then standard nonparametric results are obtainable for measurement error models. New and constructive methods to take full advantage of the compact support assumption via spline-assisted semiparametric methods are proposed. It is proven that the new estimator achieves the usual nonparametric rate as if there were no measurement error. Furthermore it is shown that similar spline approach can be used to assess the probability density function on a compact set, using observations with error, and the resulting estimator differs from the true density function only by a constant scale. In addition, it retains the nonparametric density convergence properties of the error free case. The performance of the new methods is demonstrated through simulations and the methods are implemented to analyze the relation between asthma and pollution.

Semiparametric pseudo-score and pseudo-likelihood for evaluating correlate of protection in vaccine trials.

In vaccine clinical trials, vaccine efficacy endpoint analysis is usually associated with in high cost or extended study duration, due to the generally low infection rate. Correlate of protection (CoP), which refers to surrogate endpoint, usually immunological response, that can reliably predict the treatment effect, provides a more efficient and less costly approach to evaluate the vaccine. To handle the challenge of the missingness in the unobserved surrogate immune biomarker, the pseudo-score (PS) method, semiparametric method and pseudo-likelihood (PL) method demonstrated their advantages on different aspects. In this article, we propose new methodologies to combine the advantages of PS and PL with semiparametric methods respectively, to achieve higher estimate efficiency, allow continuous baseline predictor variable, and handle multiple surrogate markers. The advantage of our methodologies are demonstrated by a simulation study in different settings and applied to a case study, which eventually can improve the chance of a successful trial.