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The purpose of this paper is to estimate aircraft roll angle and rate gyro biases using aircraft kinematics with GPS and low‐quality rate gyros.

The proposed method is motivated by observing the fact that, in typical flight applications with transport, reconnaissance, or surveillance missions, the aircraft flies along relaxed flight paths, and the associated aircraft motion can be considered as the sum of the coordinated flight, low‐frequency motion and non‐coordinated, high‐frequency motion. The proposed scheme utilizes the coordinated flight kinematics to form the relatively simple, low‐order Kalman filter that estimates the aircraft roll attitude and rate gyro biases.

The associated frequency analysis reveals that, for the estimation in the relatively low‐frequency region, the method relies primarily on GPS with the help of coordinated flight kinematics in removing the bias effect from the low‐quality rate gyros. Also, for the estimation in the high‐frequency region the method relies mainly on the numerical integration of the rate gyro for the roll attitude, which enables a high‐bandwidth estimation.

The proposed method is not suitable for flight manoeuvres where a non‐coordinated flight, such as steady heading sideslip, is sustained for a long period of time.

The proposed method has been implemented in a small UAV. The associated flight tests and simulations indicate that the new method has a potential to be used as a backup or a replacement for other complex conventional methods for many flight applications.

This paper has been the first to promote the estimation method that combines aircraft kinematics with GPS and low‐quality rate gyros.

A regression kink design (RKD or RK design) can be used to identify casual effects in settings where the regressor of interest is a kinked function of an assignment variable. In this chapter, we apply an RKD approach to study the effect of unemployment benefits on the duration of joblessness in Austria, and discuss implementation issues that may arise in similar settings, including the use of bandwidth selection algorithms and bias-correction procedures. Although recent developments in nonparametric estimation (Calonico, Cattaneo, & Farrell, 2014; Imbens & Kalyanaraman, 2012) are sometimes interpreted by practitioners as pointing to a default estimation procedure, we show that in any given application different procedures may perform better or worse. In particular, Monte Carlo simulations based on data-generating processes that closely resemble the data from our application show that some asymptotically dominant procedures may actually perform worse than “sub-optimal” alternatives in a given empirical application.

It is shown in the literature that the Arellano–Bond type generalized method of moments (GMM) of dynamic panel models is asymptotically biased (e.g., Hsiao & Zhang, 2015; Hsiao & Zhou, 2017). To correct the asymptotical bias of Arellano–Bond GMM, the authors suggest to use the jackknife instrumental variables estimation (JIVE) and also show that the JIVE of Arellano–Bond GMM is indeed asymptotically unbiased. Monte Carlo studies are conducted to compare the performance of the JIVE as well as Arellano–Bond GMM for linear dynamic panels. The authors demonstrate that the reliability of statistical inference depends critically on whether an estimator is asymptotically unbiased or not.

It is well documented that firms that are neglected by analysts and large institutions provide superior investment performance. This paper studies whether that effect is caused by an upward bias in analyst earnings forecasts. The idea is that the more popular firms are the ones with the greatest earnings estimation bias. It was found that after controlling for earnings estimation bias the neglect effect was considerably weakened. However, it was also found that there was no relation between analyst following and earnings estimation bias.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

The purpose of this paper is to carry out a meta-regression analysis upon the literature that examines the relationship between family firms and financial performance.

Information of publication and study characteristics from 61 primary studies, comprising 726 size effects was collected. In particular, three leading factors highlighted in narrative literature reviews analyzed were: the financial performance measures, the family–firm definitions and the estimation methodologies.

Overall, a positive relationship between family involvement and financial performance was found. A series of results, those linked to return on assets (ROA) – earnings before interest, taxes, depreciation and amortization (EBITDA), suggest positive publication bias from family definition and negative publication bias when OLS is used. Tobin’s Q estimates show no linkage to certain traits and aspects of the research process.

Existing literature review and meta-analysis studies show not concluding results on the family effect upon firm performance. The meta-regression analysis used in this paper allows to examine simultaneously effect size and publication bias. The latter effect is particularly salient in the approach and findings, and not present in previous studies.

Llevar a cabo un análisis de meta-regresión a la literatura que examina la relación entre firmas familiares y desempeño financiero.

Se usa la información de la publicación y características del estudio de 61 estudios primarios, que incluyen 726 estimaciones. Se examinan tres elementos principales de esta literatura: (i) medidas de desempeño financiero, (ii) definición de firma familiar, y (iii) metodología de estimación.

Se establece una relación positiva entre involucramiento familiar y desempeño financiero. Las estimaciones que examinan ROA-EBITDA sugieren sesgo positivo de publicación. Las estimaciones que utilizan estimación de Mínimos Cuadrados Ordinarios sugieren un sesgo negativo de publicación. Las estimaciones que examinan la Q de Tobin, no sugieren relación con las características de los estudios o de la investigación.

Los estudios de meta-análisis existentes sobre esta literatura no ofrecen resultados concluyentes del efecto de las firmas familiares y desempeño financiero. El método de meta-regresión permite examinar simultáneamente el efecto entre las variables y la posible existencia de sesgo de publicación. La indagación de este último es de particular interés y no se encuentra en otros estudios.

The purpose of this paper is to deal with the anomaly detection problem in multi-unmanned aerial vehicles (UAVs) formation that can be transformed to identify some unknown parameters; a more general nonlinear dynamical model for each UAV is considered to include two terms. Due to an unknown parameter corresponding to the normal or abnormal state for each UAV, the bias-compensated approach is proposed to obtain the unbiased parameter estimation. Meanwhile, the biased error and accuracy analysis are also given in case of strict statistical description of the uncertainty or white noise. To relax this strict statistical description on external noise, an analytic center approach is proposed to identify the unknown parameters in presence of bounded noise, such that two inner and outer ellipsoidal approximations are constructed to include the membership set. To be precise, this paper is regarded as one extension and summary for the author’s previous research on the anomaly detection in multi-UAV formation. Finally, one simulation example is given to confirm the theoretical results.

Firstly, one extended nonlinear relation is constructed to embody the mutual relationship of UAVs. Secondly, to obtain the unbiased parameter estimations, the bias-compensated approach is applied to achieve it under the condition of white noise. Thirdly, in case of unknown but bounded noise, an analytic center approach is proposed to deal with this special case. Without loss of generality, the author thinks this paper can be used as one extension and summary for research on multi-UAVs formation anomaly detection.

An anomaly detection problem in multi-UAVs formation can be transformed into a problem of nonlinear system identification, and in modeling the nonlinear dynamical model for each UAV, two terms are considered simultaneously to embody the mutual relationships with other nearest UAV.

To the best knowledge of the authors, this problem of the anomaly detection problem in multi-UAVs formation is proposed by the authors’ previous work, and the problem of multi-UAVs formation anomaly detection can be transferred into one problem of parameter identification. In case of unknown but bounded noise, an analytic center approach is proposed to identify the unknown parameters, which correspond to achieve the goal of the anomaly detection.

For kernel-based estimators, smoothness conditions ensure that the asymptotic rate at which the bias goes to zero is determined by the kernel order. In a finite sample, the leading term in the expansion of the bias may provide a poor approximation. We explore the relation between smoothness and bias and provide estimators for the degree of the smoothness and the bias. We demonstrate the existence of a linear combination of estimators whose trace of the asymptotic mean-squared error is reduced relative to the individual estimator at the optimal bandwidth. We examine the finite-sample performance of a combined estimator that minimizes the trace of the MSE of a linear combination of individual kernel estimators for a multimodal density. The combined estimator provides a robust alternative to individual estimators that protects against uncertainty about the degree of smoothness.

The psychological literature on subjective probability estimation is reviewed to determine the feasibility of designing probabilistic information retrieval systems using such estimates. Their use has been considered by some writers, but psychological issues have not been addressed. Research pertinent to probabilistic information retrieval is examined and implications for probabilistic information retrieval are drawn. It is shown that accurate human probability estimation is possible, both in the laboratory and in real world tasks, e.g., in meteorological forecasting; but that it is also a task subject to systematic bias, or inaccuracy. Proposed techniques for debiasing are considered. The highly task‐dependent nature of such estimates is also discussed; two implications are that results from laboratory studies may have limited relevance to real world tasks and that empirical studies specific to the context of information retrieval need to be made. Human probability estimation appears to be a difficult task, but one which can be done well with proper training and use of debiasing techniques. It is premature to say how useful such estimates would be in probabilistic information retrieval, but their use should not yet be ruled out.

We argue that resource-based view (RBV) researchers must take into account three interdependencies, (i) intrafirm resource complementarity, (ii) interfirm resource complementarity or rivalry, and (iii) compatibility or incompatibility of firm resources to broader socio-economic institutions, when attempting to empirically verify the RBV. However, these interdependencies lead to three potential causes of statistical bias, which can reduce the interpretability of such empirical examinations. First, omitted variable bias results from a researcher's inability to find and include in empirical analyses appropriate operationalizations of constructs. Second, selection bias can arise when a researcher samples only from one subset of the population, and not others. Bias in estimates can occur if a correlation between unobserved determinants of the outcome and factors affecting the selection process exist. Finally, joint dependence, where two explanatory variables are themselves mutual determinants, can lead to biased estimation.