Asymptotic Parameter Estimation Research Articles

Technical Efficiency in Swine Production Under Different Waste Management Techniques

In spite of the enormous benefits derivable from pig production, farmers are yet to fully maximize output due largely to inefficiency in the use of resources. An empirical study was carried out to examine the technical efficiency of resource use in swine production under different waste management strategies. Multistage sampling procedure was employed to select 60 farmers and primary data were obtained with the aid of questionnaire. Using the stochastic production frontier function based on the Cobb- Douglas form, asymptotic parameter estimates were evaluated to describe efficiency determinants. Result revealed that the most vital factors influencing technical efficiency across the pig farms were labour, medication and stocking density whereas the most critical explainers of inefficiency across all the pig farms were farming experience, access to credit and extension contact. The average efficiency for pig farmers that bury, dump and compost waste were 0.87, 0.88 and 0.74 respectively while the mean efficiency across all the pig farms was 0.80. The fact that all the pigs farms had mean efficiencies of less than one is indicative that none of the pig farms reached the frontier threshold in production. Thus, within the context of efficient agricultural production, pig farmers could still increase their output using appropriate technologies and the right resource-mix.

Synchronization And State Estimation Of Nonlinear Systems With Unknown Time-Delays: Adaptive Identification Method

The paper studies a novel adaptive identifier proposed in IFAC World Congress 2020 for nonlinear time-delay systems composed of linear, Lipschitz and non-Lipschitz components. To begin with, an identifier is designed for uncertain systems with a priori known delay values, and then it is generalized for systems with unknown delay values. The algorithm ensures the asymptotic parameter estimation and state observation by using gradient algorithms. The unknown delays and plant parameters are estimated by using a special equivalent extension of the plant equation. The algorithms stability is presented by solvability of linear matrix inequalities. Simulation results are invoked to support the developed identifier design and to illustrate the efficiency of the proposed synthesis procedure.

Adaptive Identification of Nonlinear Time-delay Systems Using Output Measurements

A novel adaptive identifier is developed for nonlinear time-delay systems composed of linear, Lipschitz and non-Lipschitz components. To begin with, an identifier is designed for uncertain systems with a priori known delay values, and then it is generalized for systems with unknown delay values. The algorithm ensures the asymptotic parameter estimation and state observation by using gradient algorithms. The unknown delays and plant parameters are estimated by using a special equivalent extension of the plant equation. The algorithms stability is presented by solvability of linear matrix inequalities.

Comparison of five different ways to assimilate data for a simplistic weakly nonlinear parameter estimation problem

Four non-iterative parameter estimation methods, applying four different ways to assimilate data, are compared to the (iterative) randomized maximum likelihood (RML) method on problems where the forward model is weakly nonlinear and simplistic. Both original and slightly modified versions of established data assimilation methods used for reservoir history matching are considered. Within the Gauss-linear regime, these methods are equivalent in the sense that they all sample correctly from the posterior pdf. The modification consists of replacing the global Kalman gain by a local gain similar to the one applied with RML. This modification, and the simplicity of the forward model considered, allows for asymptotic calculations, correct to lowest order in the quantity controlling the forward-model nonlinearity, to be performed for the non-iterative methods as well as for RML. Comparison of the resulting asymptotic parameter estimates explicitly reveals interesting relations between different ways to assimilate data, that goes beyond the Gauss-linear regime. Results from extensive numerical experiments with the modified methods and with the original methods support the asymptotic results when considering the mean results over a large number of randomized experimental settings where also the forward models vary. For randomized experiments with fixed forward model settings, trends from the asymptotic results were not visible in the numerical results. Although the nonlinear forward models considered are not closely related to reservoir models, the knowledge obtained should be useful also when the latter type of models are considered.

Measuring Farm-level Technical Efficiency of Urban Agriculture in Tanzanian Towns: The Policy Implications

Technical Efficiency Indices of Urban Agriculture (UA) were determined using the stochastic frontier production function which incorporates a model of technical inefficiency effects. In 2010, farm-level data of 270 urban agriculture farmers in Tanzanian urban wards of towns of Arusha, Dar es Salaam and Dodoma was obtained using semi-structured questionnaires. The parameters were estimated simultaneously with those of the model of inefficiency effects. Using the maximum likelihood estimation technique, asymptotic parameter estimates were evaluated to describe efficiency determinants. Study results revealed that a mean technical efficiency index (TEI) of 0.72 was achieved implying that output from urban agriculture production could be increased by 28% using available technologies. Despite of urban farmers having entrepreneurial acumen, they faced several challenges in resource allocation. Land size, total variable costs, and extension service charges negatively impacted on TEI. The study recommends that the government using urban agriculture and livestock extension agents should explore profitable levels for promoting UA enterprises to ascertain profitable TEI levels and UA units.

Asymptotic Parameter Estimation for a Class of Linear Stochastic Systems Using Kalman‐Bucy Filtering

The asymptotic parameter estimation is investigated for a class of linear stochastic systems with unknown parameter θ : dXt = (θα(t) + β(t)Xt)dt + σ(t)dWt. Continuous‐time Kalman‐Bucy linear filtering theory is first used to estimate the unknown parameter θ based on Bayesian analysis. Then, some sufficient conditions on coefficients are given to analyze the asymptotic convergence of the estimator. Finally, the strong consistent property of the estimator is discussed by comparison theorem.

The determinants of cumulative endogeneity bias in multivariate analysis

The BLU properties of OLS estimators under known assumptions have encouraged the widespread use of OLS multivariate regression analysis in many empirical studies that are based upon a conceptual model of a single explanatory equation. However, such a model may well be an imperfect empirical approximation to the valid underlying conceptual model, that may well contain several important additional inter-relationships between the relevant variables. In this paper, we examine the conditions under which we can predict the direction of the resultant endogeneity bias that will prevail in the OLS asymptotic parameter estimates for any given endogenous or predetermined variable, and the extent to which we can rely upon simple heuristics in this process. We also identify the underlying structural parameters to which the magnitude of the endogeneity bias is sensitive. The importance of such sensitivity analysis has been underlined by an increasing awareness of the inability of standard diagnostic tests to shed light upon the extent of the endogeneity bias, rather than upon merely its existence. The paper examines the implications of the analysis for statistical inferences about the true value of the regression coefficients and the validity of associated t -statistics.

Estimating Technical Efficiency of Waterleaf Production in a Tropical Region

Abstract Information on economics of production of minor vegetables is largely limited. This is the case for waterleaf [Talinum triangulare (Jacq). Wild]. Farm-level, output-oriented technical efficiency indices were estimated by fitting stochastic production frontier functions to survey data collected from waterleaf producers. By use of the Maximum Likelihood analysis, asymptotic parameter estimates were evaluated to describe efficiency determinants. The most critical production factors were labor, irrigation, and use of organic manure. The mean efficiency term, 0.65, indicates that waterleaf output could be increased with available technology. Some socioeconomic factors were evaluated to determine their influences on the ability of producers to be efficient in input use. Waterleaf production may be able to contribute to income derived from vegetable production in tropical environments.

Statistical Estimation of Switching Systems under Transient Conditions and Applications in Reliability Models

A new approach is proposed to problems of asymptotic parameter estimation in stochastic switching systems. In this approach, results on the asymptotic behavior of solutions of stochastic equations and theorems of the type of averaging are used for switching processes. This approach allows one to investigate asymptotic properties of estimates of parameters constructed from observed trajectories for a numerous class of stochastic systems with regular behaviors of trajectories under stationary and transient conditions.

Design of Quantizers for Decentralized Estimation Systems with Noisy Channels

Abstract We consider parameter estimation in decentralized estimation systems with noisy channels. In such systems, the observations are quantized and transmitted to the fusion center through noisy communication channels. Since the outputs of the quantizers are very sensitive to channel noises, it is important to study the effects of the channel noises on the performance of the quantizers. By assuming that the lengths of the quantization intervals become arbitrarily small, we derive the asymptotic parameter estimation error for the decentralized estimation systems. The asymptotic error includes interaction terms which depend on both the quantization errors and the channel errors. The most significant property of the interaction terms is that they can be negative. This implies that for very noisy channels, using coarser quantizers can actually decrease the parameter estimation error.