Top PDF Parametric and non-parametric forest biomass estimation from PolInSAR data

Parametric and non-parametric forest biomass estimation from PolInSAR data

Parametric and non-parametric forest biomass estimation from PolInSAR data

ABSTRACT Biomass estimation performance from model-based polari- metric SAR interferometry (PolInSAR) using generic para- metric and non-parametric regression methods is evaluated at L- and P-band frequencies over boreal forest. PolInSAR data is decomposed into ground and volume contributions, esti- mating vertical forest structure, and using a set of obtained parameters for biomass regression. The considered estima- tion methods include multiple linear regression, support vec- tor machine and random forest. The biomass estimation per- formance is evaluated on DLR’s airborne SAR data at L- and P-bands over Krycklan Catchment, a boreal forest test site in Northern Sweden. The combination of polarimetric indi- cators and estimated structure information has improved the root mean square error (RMSE) of biomass estimation up to 28% at L-band and up to 46% at P-band. The cross-validated biomass RMSE was reduced to 20 tons/ha.
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Species-specific forest variable estimation using non-parametric modeling of multi-spectral photogrammetric point cloud data

Species-specific forest variable estimation using non-parametric modeling of multi-spectral photogrammetric point cloud data

At stand level, the accuracy obtained here were 7.4% RMSE (in percent of the surveyed mean) for tree height, 13.2% for stem volume and 11.4% for basal area. These accuracies are higher, or in agreement with, results from previous studies using other operational techniques or methods for forest management planning data acquisition, such as ALS-based and subjective field methods (e.g., Ståhl, 1998; Næsset et al., 2004; Bohlin et al., 2012). Furthermore, the species-specific volume estimates showed marginally lower absolute accuracy compared to Packalén and Maltamo (2007) for spruce, but similar for pine and deciduous volume. Measured in relative terms, the results are not as comparable, though, but probably due to the large differences in field sampled mean values. Furthermore, this pilot study was carried out using a simplified framework compared to the thorough study performed by Packalén et al. (2009). Here, estimation was performed using imputation, as a means to preserve the natural dependencies between estimated variables. That is, k-MSN was applied using k=1 rather than a larger value of k, which is expected to produce more accurate results. On the other hand, in order to limit the analysis, accuracy was here assessed without cross-validation, which is expected to underestimate errors. Further differences are the limited image data utilized here; only one flight strip of images was used due to the concentrated sampling design of the field plots, providing only little data from multiple overlapping images, and the fact that these images were also spectrally adjusted by Lantmäteriet. These facts are possible causes of reduced species classification accuracy compared to the results reported by Packalén and Maltamo (2007). Finally, the dataset was heavily dominated by spruce forest, probably with insufficient variation in species compositions to accurately estimate species-specific forest variables.
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A Comparison of Selected Parametric and Non Parametric Imputation Methods for Estimating Forest Biomass and Basal Area

A Comparison of Selected Parametric and Non Parametric Imputation Methods for Estimating Forest Biomass and Basal Area

related to a specific timber type. The distance is measured by creating a weight matrix derived by canonical correspondence analysis (Ohmann & Gregory, 2002). Similarly, MSN maps are created using a model that also integrates field plot data with satellite imagery. In contrast, MSN uses a canonical correlation analysis to derive a similarity function, with selected response variables, to impute data to pixels where no ground plots exist (Moeur & Stage, 1995). The k-MSN method uses the same methods as MSN, but takes an average of the k nearest neigh- bor of plots. The Random Forest (RF) imputation method creates a classification matrix and regression tree in order to find similarities between the explanatory and response variables (Crookston & Finley, 2008).
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Comparison of reliability techniques of parametric and non-parametric method

Comparison of reliability techniques of parametric and non-parametric method

A B S T R A C T Reliability of a product or system is the probability that the product performs adequately its intended function for the stated period of time under stated operating conditions. It is function of time. The most widely used nano ceramic capacitor C0G and X7R is used in this reliability study to generate the Time- to failure (TTF) data. The time to failure data are identified by Accelerated Life Test (ALT) and Highly Accelerated Life Testing (HALT). The test is conducted at high stress level to generate more failure rate within the short interval of time. The reliability method used to convert accelerated to actual condition is Parametric method and Non-Parametric method. In this paper, comparative study has been done for Parametric and Non-Parametric methods to identify the failure data. The Weibull distribution is identi- fied for parametric method; Kaplan–Meier and Simple Actuarial Method are identified for non- parametric method. The time taken to identify the mean time to failure (MTTF) in accelerating condition is the same for parametric and non-parametric method with relative deviation.
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Non-Parametric Estimation of Topic Hierarchies from Texts with Hierarchical Dirichlet Processes

Non-Parametric Estimation of Topic Hierarchies from Texts with Hierarchical Dirichlet Processes

The role of hierarchical topic models regarding text modeling and natural language processing (NLP) is very important. The hierarchical modeling of topics allows the construction of more accurate and predictive models than the ones constructed by flat models. Models of the former type are more probable to predict unseen documents, than the latter. In most text collections, such as web pages, a hierarchical model, for instance a web directory, is able to describe the structure and organization of the document collection more accurately than flat models. This, however, ultimately depends on the nature of the data set and the true generative process of the documents themselves. Assuming that the higher levels of the hierarchy capture generic topics of a particular domain, while lower-level ones focus on particular aspects of that domain, it is expected that a hierarchical probabilistic topic model would be able to “explain” or could have generated the data set. In other words, the likelihood of such a model given the data set would probably be higher than the likelihood of other flat models (Mimno et al., 2007; Li et al., 2007; Li and McCallum, 2006).
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A non parametric approach for calibration with functional data

A non parametric approach for calibration with functional data

ple recorded on a Tecator Infratec Food and Feed Analyzer. Each spectrum was sampled at 100 wavelengths uniformly spaced in the range 850–1050 nm. The composition of each meat sample was determined by analytic chemistry, so percentages of moisture, fat, and protein were associated in this way to each spec- trum. We focus on predicting the percentage of fat on the basis of the absorbency spectrum. This problem is more challenging than the ones in Section 3 where the data were generated to fulfill the conditions of the DBIC method. The data set was randomly split 100 times into training and test sets having approximately the same size. Table 4.2 reports the mean of the mean square error (MSE), and its standard deviation, over the 100 splits, for the DBIC and NWK methods.
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Parametric and non-parametric approaches for runoff and rainfall regionalization

Parametric and non-parametric approaches for runoff and rainfall regionalization

Globally, raingauges are always used to measure the depths and rates of rainfall events. The original database consisted of 21 series of daily precipitation with different lengths mostly concentrated in the nothern part of the country. There were raingauge stations in our analysis e.g. Balakot and Kotli, where within the same locality; position of the observatories were shifted. For these cases by merging the data of the observatories located in the same location, we created new series. At some sites we faced a problem of missing rainfall values. To overcome this challenge we selected a data series with less than 15% missing values [Karl et al., 1995] for a common record period of 50 years (1961-2010). The reason of adopting this strict criterion is to ensure that all of the time series data are sufficiently long so that they not only provide reliable estimates of the extreme events probability [Jones, 1997], but also cover the same record period to avoid variability in the estimation of the parameter as a result of inter annual climatic cycles. This led to a final database of 15 observatories.
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Non-Parametric Modeling of Partially Ranked Data

Non-Parametric Modeling of Partially Ranked Data

The function g is defined on the entire lattice, but when restricted to partial rankings of the same type G = { S γ π : π ∈ S n } ⊂ W ˜ n , constitutes a normalized probability distribution on G. Estimating and examining a restriction of g to a subset H ⊂ W ˜ n (note that in general H may include more than one coset space G) rather than the function p is particularly convenient in cases of large n since H is often much smaller than the unwieldy S n . In such cases it is tempting to specify the function g directly on H without referring to an underlying permutation model. However, doing so may lead to probabilistic contradictions such as g( S γ π) < g( S λ σ) for S λ σ ⊂ S γ π. To avoid these and other probabilistic contradictions, g needs to satisfy a set of linear constraints equivalent to the existence of an underlying permutation model. Figure 3 illustrates this problem for partial rankings with the same (left) and different (right) number of vertical bars. A simple way to avoid such contradictions and satisfy the constraints is to define g indirectly in terms of a permutation model p as in (9). Applied to the context of statistical estimation, we define the estimator ˆ g in terms of an estimator ˆ p of the underlying permutation model p.
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Parametric and Non-parametric Approaches to Exits from Fixed Exchange Rate Regimes

Parametric and Non-parametric Approaches to Exits from Fixed Exchange Rate Regimes

The issue of exiting from fixed to flexible regimes lies at the junction of two rich bodies of literature: regime choice and currency crisis. Levy-Yeyati et al. (2006) analyze the determinants of regime choice by testing three hypotheses: Optimum Currency Area (OCA), financial integration, and thirdly, political crutch. Countries may opt to have greater exchange rate flexibility in order to cope with changing economic conditions. From that perspective, exits can be seen as voluntary decisions. OCA theory relates regime choice to economic structure. It predicts that a pegged regime is more desirable the higher is the degree of trade integration, but harder to sustain when an economy is subject to large real shocks, i.e. terms of trade shock in the absence of price/wage flexibility and labor mobility. The financial integration view highlights the consequences of financial integration for regime choice. According to this hypothesis, as financial integration increases, a higher occurrence of flexible regimes is likely to occur. In the third framework, that of political crutch, theory suggests that fixed regimes are more likely if the country lacks a good institutional track record, but more likely if the government is too weak to sustain them (ibid. p:5).
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Application of Clustering in the Non-Parametric Estimation of Distribution Density

Application of Clustering in the Non-Parametric Estimation of Distribution Density

Simulation results The results of errors of the analysed methods for the best selected values of the parameters are presented in appendices A, B, and C. The typical models are presented. For other data models, the accuracy analysis results are similar to the presented ones. For each method, the arithmetic mean of the error calculated using 100 simulations is presented in figures. Appendix A contains the density estimation results for AKDE, PPDE, IFDE, LSDE, and SKDE methods when the primary data clustering is used. The data clustering was performed, using automated clustering software (developed by Institute of Mathematics and Infor- matics, Vilnius) which is based on the EM algorithm. Appendix B contains the density estimation results for AKDE and PPDE methods, with the preliminary data clustering in use and without it. Appendix C contains the accuracy analysis results for the density estimators. The results, obtained by means of AKDE and PPDE, are similar to those obtained by J.N. Hwang, S.R. Lay and A. Lippman, i.e., in the case of small sample sizes and heavy tails (Cauchy samples), it is better to use the kernel density estimators, in the case of large data dimensions and large sample sizes (400 and more observations), or in the case of the Gaussian distribution, better results are obtained using the projection pursuit density estima- tor. In the case of the 5-dimensional Gaussian distribution, quite good results are
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An Overview of Non Parametric Model in Tuberculosis Data

An Overview of Non Parametric Model in Tuberculosis Data

The term “survival analysis” pertains to a statistical approach designed to take into account the amount of time an experimental unit contributed to a study i.e., it is the study of time between entry into observation and a subsequent event. Survival times are data that measure follow-up time from a defined starting point to the occurrence of a given event, for example the time from the beginning to the end of a remission period or the time from the diagnosis of a disease to death. Standard statistical techniques cannot usually be applied because the underlying distribution is rarely Normal and the data are often „censored‟.
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Robust Estimation of Parametric Models for Insurance Loss Data

Robust Estimation of Parametric Models for Insurance Loss Data

Finally, the results of this dissertation motivate open problems and generate sev- eral ideas for further research. First, it is of interest to investigate the finite-sample, not only asymptotic, performance of the newly proposed estimators. Second, the results of Chapter 6 are limited to complete, grouped, and exponentially distributed data, but they could be extended to more general situations and models. Third, additional analysis involving risk measures could be undertaken as well. Finally, all the data scenarios considered in this dissertation are based on the i.i.d. assumption, which is a reasonable assumption but not the only one to consider. Some of these problems are discussed in more detail in Section 7.2.
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A Modified Dual-Baseline PolInSAR Method for Forest Height Estimation

A Modified Dual-Baseline PolInSAR Method for Forest Height Estimation

Figure 4. One example of inversion scenario for the SBPI and DBPI methods in the unit complex plane. (a) Image 1-2 as the first baseline B 1 with DBPI; (b) Image 1-3 as the first baseline B 1 with DBPI. 4.2. Cloude’s DBPI vs. Modified DBPI In this section, the performance of forest height inversion based on the modified DBPI method is presented, and the results from the DBPI method are reused for a comparative analysis. Figure 5 a,b shows the forest height inversion results from the modified DBPI method with swapped choices of the first baseline. Both figures present similar trends and partial differences with respect to the results from the DBPI method (see Figure 2 c,d)). The estimated height difference is calculated by subtracting the DBPI results from the modified DBPI results, scaled between − 5 m and 5 m, as shown in Figure 5 c,d. From these figures, it is obvious that significant differences in some specific locations are detected. Figure 5 e presents the range terrain slope over the test site calculated from the LIDAR DEM. As expected, the height difference maps present high correlation with the slope map. When the slope is positive, the results from the modified DBPI method decrease the height estimates with respect to the results from the DBPI method. That is because positive slopes yield an overestimation of forest height, and the S-RVoG model is able to compensate for slope effects. Conversely, negative slopes lead to an underestimation of estimates. Figure 6 presents the corresponding cross-validation plots of PolInSAR forest height estimates against the LIDAR forest height. Figure 6 a,b shows that the modified DBPI results present slight improvements, with a RMSE decrease of 7.10% on average. It must be noted that this small improvement is explained because the slopes of all of the forest stands are not large, with a mean value of 3.54 ◦ . In order to better understand the effect of the slope correction, 23 forest stands with relatively large range terrain slopes ( | α | > 10 ◦ ) are selected from all 362 stands, and both the
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Non-Parametric Transformation Networks for Learning General Invariances from Data

Non-Parametric Transformation Networks for Learning General Invariances from Data

The development of NPTNs offer one such design as- pect, i.e. learning non-parametric invariances and symme- tries directly from data. Through our experiments, we found that NPTNs can indeed effectively learn general invariances without any apriori information. Further, they are effective and improve upon vanilla ConvNets even when applied to general vision data as presented in CIFAR10 and ETH-80 with complex unknown symmetries. This seems to be a crit- ical requirement for any system that is aimed at taking a step towards general perception. Assuming detailed knowledge of symmetries in real-world data (not just visual) is imprac- tical and successful models would need to adapt accordingly. In all of our experiments, NPTNs were compared to vanilla ConvNet baselines with the same number of filters (and thereby more channels). Interestingly, the superior per- formance of NPTNs despite having fewer channels indicates that better modelling of invariances is a useful goal to pur- sue during design. Explicit and efficient modelling of invari- ances has the potential to improve many existing architec- tures. Indeed, we outperform several state-of-the-art algo- rithms on ETH-80. In our experiments, we also find that Capsule Networks which utilized NPTNs instead of vanilla ConvNets performed much better. This motivates and justi- fies more attention towards architectures and other solutions that efficiently model general invariances in deep networks. Such an endeavour might not only produce networks per- forming better in practice, it also promises to deepen our understanding of deep networks and perception in general.
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Non-parametric estimation of the coefficients of ergodic diffusion processes based on high-frequency data

Non-parametric estimation of the coefficients of ergodic diffusion processes based on high-frequency data

The content of this chapter is directly inspired by Comte, Genon-Catalot, and Rozenholc (2006; 2007). We consider non-parametric estimation of the drift and diffusion coefficients of a one-dimensional diffusion process. The main assumption on the diffusion model is that it is ergodic and geometrically β- mixing. The sample path is assumed to be discretely observed with a small regular sampling interval ∆. The estimation method that we develop is based on a penalized mean square approach. This point of view is fully investigated for regression models in Comte and Rozenholc (2002, 2004). We adapt it to discretized diffusion models.
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Forecasting Bank Failure: A Non-Parametric Frontier Estimation Approach

Forecasting Bank Failure: A Non-Parametric Frontier Estimation Approach

The dramatic rise in bank failures over the last decade has led to a search for leading indicators so that costly bailouts might be avoided. While the quality of a bank’s management is generally acknowledged to be a key contributor to institutional collapse, it is usually excluded from early-warning models for lack of a metric. This paper describes a new approach for quantifying a bank’s managerial efficiency, using a data-envelopment- analysis model that combines multiple inputs and outputs to compute a scalar measure of efficiency. This new metric captures an elusive, yet crucial, element of institutional success: management quality. New failure-prediction models for detecting a bank’s troubled status which incorporate this explanatory variable have proven to be robust and accurate, as verified by in-depth empirical evaluations, cost sensitivity analyses, and comparisons with other published approaches.
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Bayesian Approaches to Non-parametric Estimation of Densities on the Unit Interval

Bayesian Approaches to Non-parametric Estimation of Densities on the Unit Interval

There are some intuitive reasons why this method requires some careful considerations for estimating pdf of variables having support [0, 1]. The foregoing kernel density estimator was developed mainly for estimating densities with unbounded support. For such densities, the main interest would typically be in the middle region of the support. Because, most of the observations lie in the middle region and f e is consistent, although biased, the bias of the estimator in the tail region has not been a serious issue. When the underlying density has support [0, 1], this kernel density estimator may suffer from serious boundary bias. The estimator f e with fixed bandwidth would not be suitable because it would have support outside [0, 1]. Choosing the bandwidth sufficiently close to zero for values of x near the boundary 0, in order to avoid this, is a difficult task. A remedy may appear to be to transform the data from [0, 1] onto the entire real line, estimate the density on the real line using the large literature on this topic, and then transform the estimated density back to have support [0, 1]. However, this does not solve the problem as far as estimation of the probability density function of recovery rates is concerned. One of our main objectives is to estimate the pdf on [0, 1] with particular emphasis on the region near the lower boundary 0, because this is the region corresponding to high risk of loss. A small point-wise bias in the estimator f e over an interval in the lower tail region towards −∞ , would translate to a large bias near the lower boundary 0 when transformed back to [0, 1]. These arguments suggest that the traditional large literature surrounding f e in (1), may not be suitable for estimating pdf of recovery rates having support [0, 1].
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Floating Car and Camera Data Fusion for Non-parametric Route Travel Time Estimation

Floating Car and Camera Data Fusion for Non-parametric Route Travel Time Estimation

In light of increasing congestion in urban areas, monitoring and providing information about tra ffic conditions is critical for tra ffic management and effective transport policy. Travel time data may be collected from stationary auto- matic vehicle identification (AVI) sensors (automatic number plate recognition (ANPR) cameras, Bluetooth devices, etc.). AVI systems provide direct measurements of route travel times, but the spatial coverage is typically small and may not be representative of the network as a whole. Meanwhile, floating car data (FCD) collected from GPS devices installed in vehicle fleets or smart phones provide information from the entire network. Travel time estimation from FCD is often challenging because of low penetration rate, which means that the number of available FCD observations from vehicles traveling along the route of interest may be low if it is not a common one.
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A non Parametric Estimation of Service Level in a Discrete Context

A non Parametric Estimation of Service Level in a Discrete Context

The rest of this paper is organized as follows. Section 2 presents the most important practical issues related with the estimation of the service level considering: (i) demand distribution selection and validation using sample data even when they are scarce, (ii) alternatives for the estimation of the demand distribution parameters, and (iii) calculation of the temporal aggregates of the demand. Section 3 is devoted to introduce our proposed non parametric approach and provide some illustrative examples. Finally, conclusions and further research are presented in Section 4.
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A parametric approach to the estimation of cointegration vectors in panel data

A parametric approach to the estimation of cointegration vectors in panel data

For these reasons, a parametric approach may be a promising alternative, in particular, for panels with a small number of time periods. In this paper a vector error correction model (VECM) is employed to represent the dynamics of the system. Our framework can be seen as a panel analog of Johansen’s cointegrated vector autoregression, where the short-run parameters are al- lowed to vary across countries and the long-run parameters are homogenous. Unfortunately, in such a setup the ML estimator cannot be computed from solving a simple eigenvalue problem as in Johansen (1988). Instead, in sec- tion 2 we adopt a two-step estimation procedure that was suggested by Ahn and Reinsel (1990) and Engle and Yoo (1991) for the usual time series model. As in Levin and Lin (1993) the individual specific parameters are estimated in a first step, whereas in a second step the common long-run parameters are estimated from a pooled regression. The resulting estimator is asymp- totically efficient and normally distributed. Furthermore, a number of test procedures that are based on the two-step approach is considered in section 4 and extensions to more general models are addressed in section 5. The results of a couple of Monte Carlo simulations presented in section 6 suggest that the two-step estimator performs better than the FM-OLS estimator in typical sample sizes. Some conclusions and suggestions for future work can be found in section 7.
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