Time series modelling and maximum entropy

Ulrych, Tadeusz J.; Clayton, R.

doi:10.1016/0031-9201(76)90047-9

Cited by 414 publications

(116 citation statements)

References 19 publications

Supporting

Mentioning

110

Contrasting

Unclassified

Order By: Relevance

“…Of these, a linear regression (for the points below 900 yE m-* s-') combined with a hyperbolic tangent function proved the best fit, although even this combination produced portions of the curve which lay completely above all data points. Instead, a smooth curve was drawn by eye after applying the maximum entropy-moving average (ME-AR) technique (Ulrych & Bishop 1975, Ulrych & Clayton 1976. The saturating light level was determined to b e 1260 pE m-2 S-' by averaging the points around the asymptote (Platt et al 1975, Harding et al 1980, or approximately 60 % of summer midday sunlight.…”

Section: Photosynthesis Vs Light Intensitymentioning

confidence: 99%

Reducing the error in estimating annual production of benthic microflora: hourly to monthly rates, patchiness in space and time

Shaffer¹,

Onuf²

1985

Mar. Ecol. Prog. Ser.

View full text Add to dashboard Cite

A formula based on the measured relation between solar radiation and photosynthetic rate was developed for computation of the annual productivity of benthic microflora. The saturating light level of 1260 pE m -' s i obtained with intact sediment cores was > 5 X higher than reported in other studies. This, together with the invalid assumption that measurement periods were representative of average conditions, accounted for discrepancies among estimates when 3 formulas employed in other studies were applied to our data. The new formula developed in this study is likely to be the most reliable estimator of annual productivity, since it is based on measured relations between photosynthesis and irradiance and does not assume that measurement periods were representative of average conditions. Its drawback is that it requires laborious analysis. In addition to the annual study, intensive sampling was conducted during a single month (60 samples incubated every other day) to obtain an estimate closely approximating actual monthly production. By using this close-to-direct measurement of monthly production, we were able to assess quantitatively the sources of error likely to be introduced by converting the hourly rates to monthly production using two of the formulas. This error was compared to the error likely to be introduced by spatial heterogeneity and by temporal heterogeneity (i.e. different sampling rates). Results indicate that the error introduced by inadequate sampling in space and time outweighs the error introduced by the commonly used conversions from measured midday productivity to estimated monthly production. Compositing many small samples from a study area into each incubation chamber efficiently addressed the problem of spatial heterogeneity. Monthly production estimated from 4 sampling days per month agreed closely with monthly production determined from 16 sampling days (mean deviation 5 YO). Estimates based on 1 or 2 sampling days m o ' were not in close agreement with the 16 sampling day estimate (mean deviation 30 YO).

show abstract

Section: Photosynthesis Vs Light Intensitymentioning

confidence: 99%

Reducing the error in estimating annual production of benthic microflora: hourly to monthly rates, patchiness in space and time

Shaffer¹,

Onuf²

1985

Mar. Ecol. Prog. Ser.

View full text Add to dashboard Cite

show abstract

“…Although this property is couuuon to the forward, backward and forward-backward arrangements, the last one presents the best performance as a frequency estimator [7], · [8].…”

Section: The Fbctls Methodsmentioning

confidence: 99%

“…The Forward -Backward Linear Prediction (FBLP) method introduced by Nuttal [7] and Ulrich and Clayton [8] is particularly effective. The performance of the FBLP method were significantly improved by Kumaresan [9] through the Modified ForwardBackward Linear Prediction (MFBLP) method.…”

Section: Introductionmentioning

confidence: 99%

A DOA Estimator Based on Linear Prediction and Total Least Squares

Lopes¹,

Bonatti²,

Peres³

et al. 2002

JCIS

View full text Add to dashboard Cite

-We propose an estimator for the direction of arrival (DOA) of plane waves incident on a linear equally spaced array of sensors. The estimator nses a forward -backward linear prediction filter optimized by means of the total least squares criterion subject to constraints. The DOA angles are estimated by the zeros of the optimum filter. The proposed procedure is compared with two maximum likelihoodmethods presented in the literatnre.Keywords: Array Signal Processing, Direction of Arrival, Linear Prediction, Total Least Squares.Resumo .. Propomos urn estimador para a diret;lio de chegada (DOA) de ondas planas incidentes em urn arranjo de sensores linear e com elementos iguahnente espat;ados. 0 estimador usa urn filtro de predit;ffo linear "fmward-backward" otimizado atraves do criteria dos rninimos quadrados totais sujeito a restri<;6es. Os iingnlos DOA sao estimados atraves dos zeros do filtro otimizado. 0 procedirnento proposto e comparado com dais m6todos de maxima verossimilhant;a presentes na literatura.

show abstract

“…Burg commented on the better resolution and the more realistic power estimates of the MEM compared to the conventional methods of spectral estimation. Later Ulrych and Clayton [18] reviewed the principles of maximum entropy spectral analysis and the closely related topic of autoregressive time series modeling. Barrodale and Erickson [19,20] developed an algorithm to solve the underlying least-squares linear prediction problem in maximum entropy spectral analysis.…”

Section: Inlet Cavitymentioning

confidence: 99%

Data Adaptive Spectral Analysis of Unsteady Leakage Flow in an Axial Turbine

Barmpalias

Chokani

Kalfas

et al. 2012

International Journal of Rotating Machinery

View full text Add to dashboard Cite

A data adaptive spectral analysis method is applied to characterize the unsteady loss generation in the leakage flow of an axial turbine. Unlike conventional spectral analysis, this method adapts a model dataset to the actual data. The method is illustrated from the analysis of the unsteady wall pressures in the labyrinth seal of an axial turbine. Spectra from the method are shown to be in good agreement with conventional spectral estimates. Furthermore, the spectra using the method are obtained with data records that are 16 times shorter than for conventional spectral analysis, indicating that the unsteady processes in turbomachines can be studied with substantially shorter measurement schedules than is presently the norm.

show abstract

Time series modelling and maximum entropy

Cited by 414 publications

References 19 publications

Reducing the error in estimating annual production of benthic microflora: hourly to monthly rates, patchiness in space and time

Reducing the error in estimating annual production of benthic microflora: hourly to monthly rates, patchiness in space and time

A DOA Estimator Based on Linear Prediction and Total Least Squares

Data Adaptive Spectral Analysis of Unsteady Leakage Flow in an Axial Turbine

Contact Info

Product

Resources

About