A Comparison of Distance Function Estimation Methods for Analyzing Production Characteristics and Efficiency

Abstract In this study, to obtain optimal estimates of the earthquake hazard in North China based on the modern earthquake catalog, we used two variable kernel function estimation methods, proposed by Stock and Smith, and Zhuang, the Bayesian Delaunay tessellation smoothing method by Ogata (ODTB), and a newly proposed incomplete centroidal Voronoi tessellation (ICVT) method, to calculate the total and background seismic spatial occurrence rates for the study area. The sophisticated ODTB method is more stable than the others, but is relatively expensive, in terms of computation demands, whereas Zhuang et al.’s kernel estimate and the new ICVT method are able to provide reasonable estimates and easier to implement. We also calculated the spatial variations of the b‐value, using the Bayesian method with smoothness prior proposed by Ogata. Using comparative analyses and simulation experiments, we show that all of the methods give similar spatial patterns of seismic occurrences.

Download Full-text

Evaluation of Hantush’s S Function Estimation Methods for Predicting Rise in Water Table

Water Resources Management ◽

10.1007/s11269-019-02272-1 ◽

2019 ◽

Vol 33 (7) ◽

pp. 2239-2260

Author(s):

Shakir Ali ◽

Adlul Islam

Keyword(s):

Water Table ◽

Estimation Methods ◽

Function Estimation

Download Full-text

Discrete vector quantization for arbitrary distance function estimation

IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) ◽

10.1109/3477.704289 ◽

1998 ◽

Vol 28 (4) ◽

pp. 496-510 ◽

Cited By ~ 6

Author(s):

J. Oommen ◽

I.K. Altinel ◽

N. Aras

Keyword(s):

Vector Quantization ◽

Distance Function ◽

Function Estimation

Download Full-text

Comparison of Nonparametric Function Estimation Methods for Discontinuous Regression Functions

Korean Journal of Applied Statistics ◽

10.5351/kjas.2010.23.6.1245 ◽

2010 ◽

Vol 23 (6) ◽

pp. 1245-1253

Author(s):

Dong-Ryeon Park

Keyword(s):

Estimation Methods ◽

Function Estimation ◽

Nonparametric Function ◽

Regression Functions ◽

Nonparametric Function Estimation

Download Full-text

Independence-aware Advantage Estimation

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/461 ◽

2021 ◽

Author(s):

Pushi Zhang ◽

Li Zhao ◽

Guoqing Liu ◽

Jiang Bian ◽

Minlie Huang ◽

...

Keyword(s):

Monte Carlo ◽

Reinforcement Learning ◽

Importance Sampling ◽

Time Horizon ◽

Estimation Methods ◽

Function Estimation ◽

Complex Environments ◽

Estimation Variance ◽

Independence Property ◽

Current Action

Most of existing advantage function estimation methods in reinforcement learning suffer from the problem of high variance, which scales unfavorably with the time horizon. To address this challenge, we propose to identify the independence property between current action and future states in environments, which can be further leveraged to effectively reduce the variance of the advantage estimation. In particular, the recognized independence property can be naturally utilized to construct a novel importance sampling advantage estimator with close-to-zero variance even when the Monte-Carlo return signal yields a large variance. To further remove the risk of the high variance introduced by the new estimator, we combine it with existing Monte-Carlo estimator via a reward decomposition model learned by minimizing the estimation variance. Experiments demonstrate that our method achieves higher sample efficiency compared with existing advantage estimation methods in complex environments.

Download Full-text

A Structural Model of a Multitasking Salesforce: Incentives, Private Information, and Job Design

Management Science ◽

10.1287/mnsc.2021.4079 ◽

2021 ◽

Author(s):

Minkyung Kim ◽

K. Sudhir ◽

Kosuke Uetake

Keyword(s):

Adverse Selection ◽

Private Information ◽

Structural Model ◽

Job Design ◽

Estimation Methods ◽

Efficiency Gain ◽

Function Estimation ◽

Task Specialization ◽

Heterogeneous Effects ◽

Two Step Estimation

This paper broadens the focus of empirical research on salesforce management to include multitasking settings with multidimensional incentives, where salespeople have private information about customers. This allows us to ask novel substantive questions around multidimensional incentive design and job design while managing the costs and benefits of private information. To this end, the paper introduces the first structural model of a multitasking salesforce in response to multidimensional incentives. The model also accommodates (i) dynamic intertemporal tradeoffs in effort choice across the tasks and (ii) salesperson’s private information about customers. We apply our model in a rich empirical setting in microfinance and illustrate how to address various identification and estimation challenges. We extend two-step estimation methods used for unidimensional compensation plans by embedding a flexible machine learning (random forest) model in the first-stage multitasking policy function estimation within an iterative procedure that accounts for salesperson heterogeneity and private information. Estimates reveal two latent segments of salespeople—a hunter segment that is more efficient in loan acquisition and a farmer segment that is more efficient in loan collection. Counterfactuals reveal heterogeneous effects: hunters’ private information hurts the firm as they engage in adverse selection; farmers’ private information helps the firm as they use it to better collect loans. The payoff complementarity induced by multiplicative incentive aggregation softens adverse specialization by hunters relative to additive aggregation but hurts performance among farmers. Overall, task specialization in job design for hunters (acquisition) and farmers (collection) hurts the firm as adverse selection harm overwhelms efficiency gain. This paper was accepted by Duncan Simester, marketing.

Download Full-text

The Substitution Elasticity, Factor Shares, and the Low-Frequency Panel Model

American Economic Journal Macroeconomics ◽

10.1257/mac.20140302 ◽

2017 ◽

Vol 9 (4) ◽

pp. 225-253 ◽

Cited By ~ 19

Author(s):

Robert S. Chirinko ◽

Debdulal Mallick

Keyword(s):

Low Frequency ◽

Estimation Methods ◽

Function Estimation ◽

Pass Filter ◽

Low Pass Filter ◽

Long Run ◽

New Strategy ◽

Low Pass ◽

Secular Decline ◽

Production Function Estimation

The value of the elasticity of substitution between labor and capital (σ) is a crucial assumption in understanding the secular decline in the labor share of income. This paper develops and implements a new strategy for estimating this crucial parameter by combining a low-pass filter with panel data to identify the low-frequency/long-run relations appropriate to production function estimation. Standard estimation methods, which do not filter out transitory variation, generate downwardly biased estimates of 40 percent to 70 percent relative to the benchmark value. Despite correcting for this bias, our preferred estimate of 0.40 is substantially below the Cobb-Douglas assumption of σ = 1. (JEL C51, E22, E24, E25, O41)

Download Full-text