Efficient estimation of quasi-likelihood models using B-splines

For many medical questions, X-ray imaging belongs to the gold standard for diagnosis, treatment planning, treatment guidance, and surgery assessment. To improve the reading performance, standardized image rotation is an important step. We propose a new algorithm to estimate the correct image rotation. For many body regions, one line can be defined that is aligned with the upright orientation of the X-ray image. This line can be, for example, the shaft axis of a long bone or the axis of the spine. In this paper, we propose a strategy to automatically align X-ray images with their standard orientation. In a first step, the heatmap of this line is determined using the segmentation network D-LinkNet. The rotation direction, up to a top-down flip, is obtained by computing the orientation of the main axis of this heatmap. For the orientation computation, we compare PCA and Hu moments. While the PCA requires to threshold the heatmap, Hu moments can be used directly on the output values of the network, preserving the (un)certainty of the segmentation. We compare these two methods with a ResNet-18 for the direct estimation of the image rotation on 220 X-ray images from the MURA dataset showing the wrist in the AP view. With the heatmap-based approach followed by Hu moments analysis, the median absolute error for the angle estimation can be reduced to 0.7° compared to 1.7° by a direct estimation method. PCA suffers from noisy heatmaps for images of bad quality degrading the overall performance of this approach.

Trunk extension exercises: How is trunk extensor muscle recruitment related to the exercise dosage?

In this paper we investigate the block- type pilot channel estimation for orthogonal frequency division multiplexing (OFDM) systems. The estimation is based on the minimum mean square error (MMSE) estimator and the least square (LS) estimator. We derive the MMSE and LS estimators’ architecture and investigate their performances. We prove that the MMSE estimator performance is better but computational complexity is high, contrary the LS estimator has low complexity but poor performance. For reducing complexity we proposed two different solutions which are the Simplified Least Square (SLS) estimator and the modified MMSE estimator. In the SLS estimator, we apply an auto-correlation function with the LS estimator to remove the noise. In the modified MMSE estimator, we consider only the significant energy samples and ignore the remaining noisy samples. Based on this idea we introduce the modified MMSE estimator. We evaluate estimator’s performance on basis of mean square error and symbol error rate for 16 QAM systems using MATLAB.

Research on the Method of Orbit Determination Based on the Self-adaptive Stable Least p-norm Estimate

Diffusion is a basic mathematical tool for modern financial engineering. The theory of the estimation methods for diffusion models is an important topic of the financial engineering. Many researches have been tried to apply the likelihood estimation method for estimating diffusion models. However, the likelihood estimation method for diffusion is complicated and needs much amount of computing. In this paper we develop the estimation methods which are simple enough to be compared to the Euler approximation method, and efficient enough statistically to be compared to the likelihood estimation method. We devise pseudo-likelihood and propose the maximum pseudo-likelihood estimation methods. The pseudo-likelihoods are obtained by approximating the transition density with normal distributions. The means and the variances of the distributions are obtained from the delta expansion suggested by Lee, Song and Lee (2012). We compare the newly suggested estimators with other existing estimators by simulation study. From the simulation study we find the maximum pseudo-likelihood estimator has very similar properties with the maximum likelihood estimator. Also the maximum pseudo-likelihood estimator is easy to apply to general diffusion models, and can be obtained by simple numerical steps.

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally; we display new estimators that presented by Youssef and Abonazel [40] as more efficient estimators than the conventional estimators.

The historical background of the subject is given, together with a discussion of the types of scales with which attempts have been made to measure subjective brightness. The apparatus used for direct estimation experiments is described, and the method of direct estimation is explained in detail. In this method the observer is presented with a series of test fields in random order of luminance and asked to grade them by directly estimating the magnitude of their apparent brightness. The method was exhaustively explored and the effects were found of different groups of observers, the size of luminance step, the order of presentation of the luminances, field size, foveal and extra foveal viewing, and the colour of the test field. Scaling is discussed, and statistical evidence showing that the directly estimated brightnesses are on a ratio scale is given. Field work confirmed that the apparent brightness data obtained with laboratory apparatus is applicable to practical situations. The effect of the adaptation field is discussed. Binocular matching experiments were used to relate more accurately apparent brightness data to different adaptation levels. Apparent brightness scales for light different adaptation levels are given which can be used for the luminance design of interiors.

This paper concerns with various techniques for estimations from the generalized linear exponential distribution (GLED) that can be used for modeling bathtub, increasing and decreasing hazard rate (HR) behavior and was first proposed by [3]. This distribution is important since it contains as special sub-models some widely well-known distributions such as the exponential distribution (ED), the Rayleigh distribution (RD), the linear exponential distribution (LED), and the Weibull distribution (WD). The various techniques for estimations can be considered as maximum likelihood estimation (MLE), least-square estimation (LSE), weighted least square estimation (WLSE), Cramer Von-Mises estimation (CVME), and Anderson Darling estimation (ADE). These methods of estimations are used to estimate the unknown parameters of the well-known GLED. Two applications are used to show that the GLED is a viable distribution in modeling lifetime data and to compare the varying methods of estimations based on the Kolmogorov-Simnorov test with the corresponding P-value to show the optimal method. Finally, a simulation study is presented to compare the varying methods of estimation based on the mean square error (MSE) and the average absolute bias (AAB).

This paper employs a new estimation method for estimating stage–discharge rating curve parameters. In typical practical applications, the original non-linear rating curve is transformed into a simple linear regression model by log-transforming the measurement without examining the effect of heteroscedasticity of residuals. Therefore, the model with pseudo-likelihood estimation is developed in this study to deal with heteroscedasticity of residuals in the original non-linear model. The parameters of rating curves and variance functions of errors are simultaneously estimated by the pseudo-likelihood estimation (P-LE) method. Also simulated annealing, a sort of global optimization techniques, is adapted to minimize the log likelihood of the weighted residuals. At first, the developed P-LE model was applied to a hypothetical site where stage–discharge data were generated by incorporating various errors for statistical test. Also, the limit of stages for segmentation is estimated in the process of P-LE using the Heaviside function. For the validation of effects of the developed P-LE model, the results of the conventional log-transformed linear regression (LT-LR) model and the P-LE model are estimated and compared. After statistical simulation, the developed P-LE model is then applied to the real data sets from six gauge stations in the Geum River basin. It can be suggested that this new estimation method is applied to real river sites to successfully determine the weights taking into account error distributions from observed discharge data.

Key words: tropical forest, conservation management, local livelihoods, forest cover change, disturbance, fuelwood, forest structure, species richness, biomass, Mount Elgon A growing world population has important consequences for forests. In this study I investigate how conflicting goals by different actors under different historical contexts impacted the protected area of Mt Elgon, Uganda, and I consider what this means for conservation. Mt Elgon is an important water catchment area for Uganda and Kenya with important biodiversity values. The forest on Mt Elgon is also a source of agricultural land, timber, fuel wood and other forest resources for local communities. In this study I explore the factors that influenced local people’s motivations for forest clearing, the impacts of local forest use, including as a source of fuelwood, on Mt Elgon, Uganda. I also evaluate the use of radar satellite data to estimate above ground biomass on Mt Elgon. Finally I discuss the implications for the design of interventions that seek to reconcile the needs of local people and forest conservation. A major wave of deforestation on Mt Elgon, Uganda took place in the 1970s and 1980s and by 2009, 25% of the forest on Mt Elgon was lost. However, locally, there were areas of recovery. This study demonstrated that agricultural expansion on Mt Elgon cannot simply be linked to individual drivers such as population or high crop prices, and these were not always associated with increased deforestation. By analysing local variations, I found that it is the context (institutional, social, political) under which drivers such as population, wealth or commodity prices operate, rather than the drivers per se, that influences outcomes for forest cover. I found that local forest uses strongly influenced forest structure, even where people had a collaborative management agreement with the park authorities. The type of resources collected varied with the land use systems around the park: small stem-harvesting affected regeneration in areas where people grew crops that require supports such as bananas and climbing beans, and seedlings were almost absent where in-forest cattle grazing was important. Studying the characteristics and impacts of fuelwood harvesting revealed high levels of fuelwood collection and depletion of dead wood on the edge of the park. Human impacts affected highly preferred and used tree species. Allowing the collection of fuelwood or other non-timber products creates opportunities for more destructive activities such as timber harvesting or charcoal making. On the other hand it helps to improve relations between local people and park staff, which this study showed helps limit agricultural encroachment. I also found indications that trees on people’s own land can provide alternative sources of fuel. Mt Elgon has a history of conservation and development projects in an attempt to better reconcile local livelihood improvement and forest conservation. The most recent include pilot REDD+ schemes both inside and outside the protected area. Such schemes need consistent biomass estimations. I used a cost-effective field method for direct basal area estimation that yielded consistent estimates of above ground biomass (AGB), which reached above 800Mg/ ha on Mt Elgon’s northern slopes. Radar (ALOS PALSAR) data produced realistic classifications of the different vegetation types. However, using radar backscatter values in combination with field estimated AGB data to produce a biomass map had limited success. This was likely linked to the sampling strategy and topography. Our study showed that simple theoretical models based on single drivers of deforestation cannot explain local variation, nor can simple models that lead to “simplified institutional prescriptions” lead to sustainable solutions, as they do not reflect complex local social and ecological realities. This has important implications for the design of more locally adapted and ecologically and socially sustainable management arrangements on Mt Elgon and elsewhere. These are necessary because current practices appear to lead to forest degradation and resource depletion. Building trust between stakeholders and developing alternative resources are vital to support more sustainable forest management. Both international conservation actors, as well as forest management authorities need to recognise that incentives that influence people’s motivation for action vary locally and can therefore not be designed globally.

The quasi-likelihood function proposed by Wedderburn [Quasi-likelihood functions, generalized linear models, and the Gauss–Newton method. Biometrika. 1974;61:439–447] broadened the application scope of generalized linear models (GLM) by specifying the mean and variance function instead of the entire distribution. However, in many situations, complete specification of variance function in the quasi-likelihood approach may not be realistic. Following Fahrmeir's [Maximum likelihood estimation in misspecified generalized linear models. Statistics. 1990;21:487–502] treating with misspecified GLM, we define a quasi-likelihood nonlinear models (QLNM) with misspecified variance function by replacing the unknown variance function with a known function. In this paper, we propose some mild regularity conditions, under which the existence and the asymptotic normality of the maximum quasi-likelihood estimator (MQLE) are obtained in QLNM with misspecified variance function. We suggest computing MQLE of unknown parameter in QLNM with misspecified variance function by the Gauss–Newton iteration procedure and show it to work well in a simulation study.

Over-dispersed count data are frequently observed in clinical trials where the primary endpoint is occurrence of clinical events. Sample sizes of comparative clinical trials with these data are typically calculated under negative binomial models or quasi-Poisson models with specified variance functions, or under the assumption that the specified "working" variance functions are correctly specified. In this article, we propose a sample size formula anticipating misspecifications of the working variance function. We derived a method based on the asymptotic distribution of a Wald test statistic with a sandwich-type robust variance estimator under quasi-Poisson models. Under misspecifications of the working variance function, the asymptotic variance of the estimator of the treatment effect is expressed as a form involving both true and working variance functions. Our sample size formula includes several existing formulas as special cases when the working variance function is correctly specified as the true variance function. We also consider a sensitivity analysis for possible misspecifications of the "true" variance function when estimating sample sizes using our formula. A simulation study demonstrated the adequacy of our formulas in finite sample size settings. An application to a clinical trial to evaluate the treatment effect on prevention of COPD exacerbation is provided.

In speech recognition and speech synthesis, accurate estimation of the pitch period is an important part of speech processing. The traditional direct peak estimation method and the autocorrelation function method are both effective time domain estimation algorithms. The autocorrelation method is a pitch period estimation algorithm suitable for low SNR. Both algorithms need to get accurate peak position estimation. In this paper, a multi-line cut method which is a method for judging the position of the peak point is proposed. The multi-line cut method is used to intercept the sampled data of the waveform by using multiple cut lines. The median value is calculated by the starting and ending points of the cut line position, and the peak position is indirectly evaluated. By minimizing the impact of interference on the peak estimate, the likelihood of falling into local extreme points is reduced, therefore a more accurate peak point estimate than the direct search for peak points can be obtained. The simulation results show that compared with the traditional direct peak estimation method, the performance of peak estimation by the multi-line cut method can be greatly improved, and the multi-line cut method can be used to estimate the peak value in the autocorrelation method, and also achieve a certain performance improvement. In addition, the number of cut lines is directly related to performance, and the more the number is, the better the performance is. The complexity of this method is not high and easy to implement.

The method of direction estimation (MODE) offers appealing advantages such as asymptotic efficiency with mild computational complexity and excellent performance in handling coherent signals, which are not shared by conventional subspace-based methods. However, the MODE employs additional assumption and constraints on the symmetry of the root polynomial coefficients, which might cause severe performance degradation in the scenario of low signal-to-noise ratio/small sample size, since any estimation error will be enlarged twice due to the symmetry. Moreover, the standard realization for MODE does not have a closed-form solution for updating its estimates. In this paper, the optimization problem of MODE is proved to be equivalent to that of the principal-eigenvector utiliztion for modal analysis (PUMA) algorithm. We show that PUMA which has closed-form solution, that does not rely on any additional assumption and constraint on the coefficients, is a better surrogate than MODE for minimizing the same cost function. Extensive simulation results are carried out to support our standpoint.

This paper presents a novel Model Predictive Direct Power Control (MPDPC) approach for the pulse width modulation (PWM) rectifiers in the Aircraft Alternating Current Variable Frequency (ACVF) power system. The control performance of rectifiers may be largely affected by variations in the AC side impedance, especially for systems with limited power volume system. A novel idea for estimating the impedance variation based on the Bayesian estimation, using an algorithm embedded in MPDPC is presented in this paper. The input filter inductance and its equivalent series resistance (ESR) of PWM rectifiers are estimated in this algorithm by measuring the input current and input voltage in each cycle with the probability Bayesian estimation theory. This novel estimation method can overcome the shortcomings of traditional data based estimation methods such as least square estimation (LSE), which achieves poor estimation results with the small samples data set. In ACVF systems, the effect on the parameters estimation accuracy caused by the number of sampling points in one cycle is also analyzed in detail by simulation. The validity of this method is verified by the digital and Hard-in-loop simulation compared with other estimation methods such as the least square estimation method. The experimental testing results show that the proposed estimation algorithm can improve the robustness and the control performance of the MPDPC under the condition of the uncertainty of the AC side parameters of the three-phase PWM rectifiers in aircraft electrical power system.