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To provide an autonomous navigation system to endow lunar rovers with increased autonomy both for exploration achievement of scientific goals and for safe navigation.

First, algorithm and technique of initial position determination of lunar rovers are introduced. Then, matched‐features set is build by multi steps of image processing such as feature detection, feature tracking and feature matching. Based on the analysis of the image processing error, a two‐stage estimation algorithm is used to estimate the motion, robust linear motion estimation is executed to estimate the motion initially and to reject the outliers, and Levenberg‐Marquardt non‐linear estimation is used to estimate the motion precisely. Next, a weighted ZSSD algorithm is presented to estimate the image disparities by analyzing the traditional ZSSD. Finally, a virtual simulation system is constructed using the development tool of open inventor, this simulation system can provide stereo images for simulations of stereo vision and motion estimation techniques, simulation results are provided and future research work is addressed in the end.

An autonomous navigation system is build based on stereo vision, the motion estimation algorithm and disparity estimation algorithm are developed.

The field test will be done in the near future to valid the autonomous navigation algorithm presented in this paper.

A very useful source of information for graduate students and technical reference for researchers who work on lunar rovers.

In this paper, stereo vision‐based autonomous navigation techniques for lunar rovers are discussed, and an autonomous navigation scheme which based on stereo vision is presented, and the validity of all the algorithms involved is confirmed by simulations.

In a multistage process, the final quality in the last stage not only depends on the quality of the task performed in that stage but is also dependent on the quality of the products and services in intermediate stages as well as the design parameters in each stage. One of the most efficient statistical approaches used to model the multistage problems is the response surface method (RSM). However, it is necessary to optimize each response in all stages so to achieve the best solution for the whole problem. Robust optimization can produce very accurate solutions in this case.

In order to model a multistage problem, the RSM is often used by the researchers. A classical approach to estimate response surfaces is the ordinary least squares (OLS) method. However, this method is very sensitive to outliers. To overcome this drawback, some robust estimation methods have been presented in the literature. In optimization phase, the global criterion (GC) method is used to optimize the response surfaces estimated by the robust approach in a multistage problem.

The results of a numerical study show that our proposed robust optimization approach, considering both the sum of square error (SSE) index in model estimation and also GC index in optimization phase, will perform better than the classical full information maximum likelihood (FIML) estimation method.

To the best of the authors’ knowledge, there are few papers focusing on quality-oriented designs in the multistage problem by means of RSM. Development of robust approaches for the response surface estimation and also optimization of the estimated response surfaces are the main novelties in this study. The proposed approach will produce more robust and accurate solutions for multistage problems rather than classical approaches.

Real estate market data often contain outliers in the observations. Since outliers have a large influence on least squares estimates, robust regression methods have been recommended for this situation. Compares the performance of least squares and least median of squares, a robust method, in the estimation of price/income relationships for apartment buildings. Multiplicative models with multiplicative errors are estimated by means of natural log transformations. The study confirms the importance of employing robust methods for this application and implies this may well be so for real estate data sets more generally.

This paper aims to test the robustness of the trend and volatility estimations for two indices: the classical Weighted Repeat Sales and a PCA factorial index. The estimations are computed from a dataset of Paris commercial properties.

First, two methodologies are presented, and then the dataset. Finally, the impact of the number of transactions per period are tested on the trend and volatility estimates for each index, and an interpretation of the results are given.

The trend and volatility estimates are biased for the WRS index and not for the PCA factorial index when the periodicity increases. Consequently, the level of the index at the end of the computing period is significantly different for various periodicities in the case of the WRS index. Globally, the PCA factorial seems to be more robust to the number of transactions.

As suggested by D. Geltner, commercial properties indices have to be built using repeat sales instead of hedonic indices. The repeat sales method is a means of constructing real estate price indices based on a repeated observation of property transactions. These indices may be used as benchmarks for real estate portfolio managers. But the investors, in general, are also interested in introducing real estate performance in their portfolio to enhance the efficient frontier. Thus, expected return and volatility are the two key parameters. To create and to improve contracts on real estate indices, trend and volatility of these indices must be robust regarding to the periodicity of the index and the volume of transactions.

Weak-form rationality of fixed-event forecasts implies that forecast revisions should not be correlated. However, significant positive correlations between consecutive forecast revisions were found in most USDA forecasts for U.S. corn, soybeans, wheat, and cotton. This study developed a statistical procedure for correction of this inefficiency which takes into account the issue of outliers, the impact of forecast size and direction, and the stability of revision inefficiency. Findings suggest that the adjustment procedure has the highest potential for improving accuracy in corn, wheat, and cotton production forecasts.

The purpose of this paper is to provide a robust version of the Malmquist Productivity Index (MPI) in order to evaluate hotels' productivity levels during the Great Recession.

Based on the order-m frontiers, we apply a robust version of an MPI. We decompose the productivity into three robust components. We use a sample of hotels operating in the Balearic Islands and Canary Islands, and we decompose and evaluate their productivity levels during the period 2004–2013. Moreover, we evaluate hotels' productivity performance during the pre-crisis period, the US subprime crisis period, the global financial crisis (GFC), the sovereign debt crisis period and the after-crisis period.

Our findings show that productivity did not deteriorate due to the adverse effects of economic crisis. This is mainly driven by increased technical change and the ability to operate at optimal scales. The long-term investment in innovation policies which are related to services and processes, appear to be the dominating feature behind hotels' productivity levels, which helped the hotel industry to recover quickly from the Great Recession.

The vast majority of empirical studies evaluating the productivity change in the hotel industry apply MPIs which are based on data envelopment analysis (DEA). However, the productivity measurement which is based on the nonparametric framework is sensitive to sample characteristics. In order to avoid such shortcomings, we apply a robust version of the MPI.

In large parts of the world, income inequality has been rising in recent decades. Other regions have experienced declining trends in income inequality. This raises the question of which mechanisms underlie contrasting observed trends in income inequality around the globe. To address this research question in an empirical analysis at the aggregate level, we examine a global sample of 73 countries between 1981 and 2010, studying a broad set of drivers to investigate their interaction and influence on income inequality. Within this broad approach, we are interested in the heterogeneity of income inequality determinants across world regions and along the income distribution. Our findings indicate the existence of a small set of systematic drivers across the global sample of countries. Declining labour income shares and increasing imports from high-income countries significantly contribute to increasing income inequality, while taxation and imports from low-income countries exert countervailing effects. Our study reveals the region-specific impacts of technological change, financial globalisation, domestic financial deepening and public social spending. Most importantly, we do not find systematic evidence of education’s equalising effect across high- and low-income countries. Our results are largely robust to changing the underlying sources of income Ginis, but looking at different segments of income distribution reveals heterogeneous effects.

The purpose of this paper is to examine the incremental information content of audit opinion while considering opinion determinants, such as auditor and auditee size, or a firm’s financial state.

A market valuation model is employed using US firm data collected over 30 years. The model relates stock returns to earnings and incorporates as additional variables auditors’ opinion types, opinion determinants and their interactions with audit expression.

The findings suggest that audit opinion has a significant market impact. The estimated positive or negative information content of the audit opinion types is associated with certain opinion determinants, such as auditor and auditee size and a firm’s financial state.

Additional firm-year observations regarding certain opinion qualifications could benefit future research.

This study offers useful insights by demonstrating the importance of auditing profession to the users of financial statements. It examines investors’ perception of each audit opinion type and the conditions under which this expression has the most serious effects. The results demonstrate the role of audit opinion and its cause-effect relationship with various economic events, allowing regulators not only to track the efficiency of various audit policy changes but also act preventively and amend the regulatory framework.

This paper empirically supports the significance of the auditing process and audit opinions by examining investor perceptions. It employs a value relevance model, in contrast to market-based research that adopts an event study methodology.

Despite the growing popularity of internet based social media on e-commerce platforms, systematic examination of the emerging phenomenon is scarce. This paper aims to study whether online retailers ' social activity on e-commerce platforms improves their business performance, and if it does, what are the underlying mechanisms.

The paper proposes a typology of online retailers ' social activities on e-commerce platforms. Then drawing on social capital theory and social network theory, the authors develop hypotheses that relate online retailers ' social activities to their business performance. The hypotheses are tested using a large dataset collected from an e-commerce platform in China.

The paper shows that: online retailers ' social activities for friend-making improve their business performance, regardless of the directional attribute of the activities; social activities for advice-seeking decrease online retailers ' business performance; and social activities for advice-giving increase online retailers ' business performance.

The data in the empirical study are from an e-commerce platform in China, hence the research results may lack generalisability. Therefore, researchers are encouraged to test the proposed hypotheses further.

The paper includes implications for e-commerce market makers and online retailers operating on e-commerce platforms. The authors show that online retailers ' social activities on e-commerce platforms can be an important source of business value. However certain types of social activities may hurt online retailers ' business performance, implying the necessity of a thoughtful social activity strategy in online marketplaces.

This paper represents an early effort to study whether online retailers ' social activities on e-commerce platforms improve their business performance and the underlying mechanisms of the effect.

Effective and robust motion estimation with sub-pixel accuracy is essential in many image processing and computer vision applications. Due to its computational efficiency and robustness in the presence of intensity changes as well as geometric distortions, phase correlation in the Fourier domain provides an attractive solution for global motion estimation and image registration. The paper aims to discuss these issues.

In this paper, relevant sub-pixel strategies are categorized into three classes, namely, single-side peak interpolation, dual-side peak interpolation and curve fitting. The well-known images “Barbara” and “Pentagon” were used to evaluate the performance of eight typical methods, in which Gaussian noise was attached in the synthetic data.

For eight such typical methods, the tests using synthetic data have suggested that considering dual-side peaks in interpolation or fitting helps to produce better results. In addition, dual-side interpolation outperforms curve fitting methods in dealing with noisy samples. Overall, Gaussian-based dual-side interpolation seems the best in the experiments.

Based on the comparisons of eight typical methods, the authors can have a better understanding of the phase correlation for motion estimation. The evaluation can provide useful guidance in this context.