Error-free Estimates Research Articles

A simulation test of the effectiveness of several methods for error-checking non-invasive genetic data

Non-invasive genetic sampling (NGS) is becoming a popular tool for population estimation. However, multiple NGS studies have demonstrated that polymerase chain reaction (PCR) genotyping errors can bias demographic estimates. These errors can be detected by comprehensive data filters such as the multiple-tubes approach, but this approach is expensive and time consuming as it requires three to eight PCR replicates per locus. Thus, researchers have attempted to correct PCR errors in NGS datasets using non-comprehensive error checking methods, but these approaches have not been evaluated for reliability. We simulated NGS studies with and without PCR error and ‘filtered’ datasets using non-comprehensive approaches derived from published studies and calculated mark–recapture estimates using CAPTURE. In the absence of data-filtering, simulated error resulted in serious inflations in CAPTURE estimates; some estimates exceeded N by[ges ]200%. When data filters were used, CAPTURE estimate reliability varied with per-locus error (Eμ). At Eμ=0.01, CAPTURE estimates from filtered data displayed <5% deviance from error-free estimates. When Eμ was 0.05 or 0.09, some CAPTURE estimates from filtered data displayed biases in excess of 10%. Biases were positive at high sampling intensities; negative biases were observed at low sampling intensities. We caution researchers against using non-comprehensive data filters in NGS studies, unless they can achieve baseline per-locus error rates below 0.05 and, ideally, near 0.01. However, we suggest that data filters can be combined with careful technique and thoughtful NGS study design to yield accurate demographic information.

A Unified Analysis for Coded DS-CDMA With Equal-Gain Chip Combining in the Downlink of OFDM Systems

This paper presents a novel unified analysis for the bit-error rate in the downlink of convolutionally coded and single-user detected multicarrier code-division multiple access (CDMA), multicarrier direct-sequence CDMA, time and frequency-domain spreading CDMA, and orthogonal frequency-division multiple access. Performance is analyzed under the assumption of Hadamard spreading codes, equal-gain chip combining, and a highly correlated frequency-selective Rayleigh-fading channel. Closed-form expressions are presented for the cumulative distribution function, probability distribution function, and moment-generating function for the signal-to-noise plus interference ratio after despreading. The presented results assume error-free channel estimates, a perfectly synchronized receiver, and are found to reasonably well agree with simulation results.

Blind estimation of channel and modulation scheme in adaptive modulation schemes for OFDM-CDMA based 4G systems

Adaptive modulation is proposed by many researchers for future generation wireless networks based on multi-carrier transmission technologies. In this paper, we propose and investigate some blind mechanisms at the receiver of the adaptive modulation based coded OFDM-CDMA systems in Rayleigh fading channels for the enhancement of the systems in terms of both the practical implementation and data throughput capacity. In this respect, blind techniques to estimate the channel impulse response and modulation scheme are proposed and investigated and their effect on the BER performance of the systems are also analyzed. A subspace based algorithm is proposed to estimate the channel impulse response blindly. This estimate is then used to coherently demodulate the OFDM-CDMA signals and adaptively switches the modulation schemes for varying channel conditions. Furthermore, an algorithm to estimate the modulation scheme at the receiver is also proposed. Results show error free estimation of modulation scheme for a maximum of 7 active users in the system at a channel condition of 20 dB. Simulation results show that in some situations, the systems based on blind channel estimation show little degradation in terms of the BER performance compared to the pilot based scheme. However, the advantages of blind estimation schemes far outweigh this degradation.

MAP Estimation of Chin and Cheek Contours in Video Sequences

An algorithm for the estimation of chin and cheek contours in video sequences is proposed. This algorithm exploits a priori knowledge about shape and position of chin and cheek contours in images. Exploiting knowledge about the shape, a parametric 2D model representing chin and cheek contours is introduced. Exploiting knowledge about the position, a MAP estimator is developed taking into account the observed luminance gradient as well as a priori probabilities of chin and cheek contours positions. The proposed algorithm was tested with head and shoulder video sequences (image resolution CIF). In nearly 70% of all investigated video frames, a subjectively error free estimation could be achieved. The 2D estimate error is measured as on average between 2.4 and .

Efficient Estimation of Volatility using High Frequency Data

The limitations of volatilities computed with daily data as well as simple statistical considerations strongly suggest to use intraday data in order to obtain accurate volatility estimates. Under a continuous time arbitrage-free setup, the quadratic variations of the prices would allow us, in principle, to construct an approximately error free estimate of volatility by using data at the highest frequency available. Yet, empirical data at very short time scales differ in many ways from the arbitrage-free continuous time price processes. For foreign exchange rates, the main difference originates in the incoherent structure of the price formation process. This market micro-structure effect introduces a noisy component in the price process leading to a strong overestimation of volatility when using naive estimators. Therefore, to be able to fully exploit the information contained in high frequency data, this incoherent effect needs to be discounted. In this contribution, we investigate several unbiased estimators that take into account the incoherent noise. One approach is to use a filter for pre-whitening the prices, and then using volatility estimators based on the filtered series. Another solution is to directly define a volatility estimator using tick-by-tick price differences, and including a correction term for the price formation effect. The properties of these estimators are investigated by Monte Carlo simulations. A number of important real-world effects are included in the simulated processes: realistic volatility and price dynamic, the incoherent effect, seasonalities, and random arrival time of ticks. Moreover, we investigate the robustness of the estimators with respect to data frequency changes and gaps. Finally, we illustrate the behavior of the best estimators on empirical data.

Development of a new quantitative approach for the isobolographic assessment of the convulsant interaction between pefloxacin and theophylline in rats.

A new mathematical approach was developed to quantify convulsant interaction between pefloxacin and theophylline in rats. Animals received each compound separately or in different combination ratios. Infusion was stopped at the onset of maximal seizures. Cerebrospinal fluid (CSF) and plasma samples were collected for HPLC drug determination. The nature and intensity of the pharmacodynamic (PD) interaction between drugs was assessed with a new modeling approach which includes (a) data transformation to create an essentially error-free X-variable and (b) estimation of an interaction parameter a by fitting a nonlinear hyperbolic model to the combination data with unweighted nonlinear regression. Drug disposition to the biophase was linear within the range of administered doses. The estimates of a suggested a Loewe antagonistic interaction between pefloxacin and theophylline at the induction of maximal seizures in rats. Similar intensity of PD interaction was observed at the dose and biophase level (alpha was -0.415 +/- 0.069 and -0.567 +/- 0.079, respectively). The suitability of the proposed model was assessed by Monte Carlo simulation. This new mathematical approach enabled the characterization of the Loewe antagonistic nature of the PD (convulsant) interaction between pefloxacin and theophylline, whereas previously used methodologies failed to do so.

A sensitivity analysis of retailer shelf management models

A shelf management model was developed to assist retailers with the decision of which products to stock and how much space to allocate to those products. Due to the non-linearities in the formulation a closed form solution is not possible. Borin, et al. develop a search heuristic based on simulated annealing and compare the solution against a known optimum. A barrier to the use of such models is the fact that managers typically do not have access to error-free estimates of the parameters required for the model construction (shelf elasticities, search loyalty, and consumer preferences). In this article we analyze the degree of error that may be introduced into estimates of the parameters before the model yields assortments and shelf allocations that are inferior to those produced by the merchandising rule of thumb, share-of-shelf = share-of-sales. The results indicate that judgmental estimates of parameters can vary by as much as 50 percent and still make application of the model useful.

Relating error bounds for maximum concentration estimates to diffusion meteorology uncertainty

The intent of this paper is to relate the magnitude of the error bounds of data, used as inputs to a Gaussian dispersion model, to the magnitude of the error bounds of the model output, which include the estimates of the maximum concentration and the distance to that maximum. The research specifically addresses the uncertainty in estimating the maximum concentrations from elevated buoyant sources during unstable atmospheric conditions, as these are most often of practical concern in regulatory decision making. A direct and quantitative link between the nature and magnitude of the input uncertainty and modeling results has not been previously investigated extensively. The ability to develop specific error bounds, tailored to the modeling situation, allows more informed application of the model estimates to the air quality issues. In this study, a numerical uncertainty analysis is performed using the Monte-Carlo technique to propagate the uncertainties associated with the model input. Uncertainties were assumed to exist in four model input parameters: (1) wind speed, (2) standard deviation of lateral wind direction fluctuations, (3) standard deviation of vertical wind direction fluctuations, and (4) plume rise. For each simulation, results were summarized characterizing the uncertainty in four features of the ground-level concentration pattern predicted by the model: (1) the magnitude of the maximum concentration, (2) the distance to the maximum concentration, and (3) and (4) the areas enclosed within the isopleths of 50% and 25% of the error-free estimate of maximum concentration. The authors conclude that the error bounds for the estimated maximum concentration and the distance to the maximum can be double that of the error bounds for individual model input parameters. The model output error bounds for the areas enclosed within isopleth values can be triple the error bounds of the input. It was not our intent to cover all possible combinations for the error in the input parameters. Ours was a much more limited goal of providing a lower bound estimate of model uncertainty in which we assume the input is reasonably well characterized and there is no bias in the input. These results allow estimation of minimum bounds on errors in model output when considering reasonable input error bounds.

Low-Error Bearing Estimation in Shallow Water

The multipath effect due to sound propagation in shallow water causes bearing errors in horizontal line arrays for all angles different from broadside when conventional resolution methods like beamformer or maximum entropy method are used. Error-free bearing estimates can be achieved only by means of generalized power estimators which use a priori knowledge about the spatial channel statistics. This a priori knowledge is usually unknown. The method described makes use of a pilot source which transmits training signals in order to achieve an estimate for a suboptimum spatial signal covariance matrix which is used for field matching.

Signal detection in Gaussian noise of unknown level: An invariance application

The concept of invariance in hypothesis testing is brought to bear on the problem of detecting signals of known form and unknown energy in Gaussian noise of unknown level. The noise covariance function is assumed to be K(t,u) = \sigma^2 \pho(t,u) where \rho(t,u) is the known form of the covariance function and \sigma^2 is the unknown level. Classical approaches to signal detection depend on the assumption that K(t,u) is known completely. Then, a correlation-type receiver that is the uniformly most powerful (UMP) test of H_o (signal absent) versus H_1 (signal present) can be derived. When \sigma^2 is unknown, there exists no UMP test. However, it is shown in this paper that there exists a test of H_o versus H_1 that is UMP-invariant for a very natural group of transformations on the space of observations. The derived test is found to be independent of knowledge about the noise level \sigma^2 , since the derived test (receiver) contains an error-free estimate of \sigma^2 . This utopian conclusion is reconciled by noting that the derived receiver can never be physically realized. It is shown that any physically realizable version of the receiver has a t -distributed test statistic. This permits choice of operating receiver thresholds and evaluation of performance characteristics.