Statistical Models for Estimating CPUE from Catch and Effort Data

1992 ◽  
Vol 49 (7) ◽  
pp. 1315-1327 ◽  
Author(s):  
Laura J. Richards ◽  
Jon T. Schnute
Keyword(s):  
Maximum Likelihood ◽  
Statistical Models ◽  
Data Sets ◽  
Central Tendency ◽  
Catch Per Unit Effort ◽  
Bivariate Model ◽  
Bivariate Models ◽  
Abundance Indices ◽  
Unique Method ◽  
Using Data

Catch-per-unit-effort (CPUE) provides one of the most commonly used abundance indices in fishery research. The literature, however, offers no unique method of estimating CPUE and its variance from catch and effort data. In this paper we develop two models (univariate and bivariate) that generalize previous approaches and remain valid under management restrictions on catch and/or effort. Both models estimate CPUE from measures of central tendency in the underlying catch and effort distributions. The models involve normalizing transformation parameters that, along with other parameters, are estimated by maximum likelihood. We illustrate the models using data from Pacific ocean perch (Sebastes alutus). For the four data sets examined, the univariate and bivariate models result in similar estimates of CPUE. However, other commonly used CPUE measures lead to inconsistent results, in particular for data sets in which catch was restricted by low trip limits. We recommend the bivariate model, since it accounts for the bivariate structure of catch and effort data. Furthermore, it can easily be adapted to accommodate alternative indices, for example, the effort required to attain a specified catch.

scholarly journals Performance of statistical methods to correct food intake distribution: comparison between observed and estimated usual intake

2016 ◽  
Vol 116 (5) ◽  
pp. 897-903 ◽  
Author(s):  
Eliseu Verly-Jr ◽  
Dayan C. R. S. Oliveira ◽  
Regina M. Fisberg ◽  
Dirce Maria L. Marchioni
Keyword(s):  
Food Intake ◽  
Statistical Methods ◽  
Statistical Models ◽  
Average Length ◽  
Data Sets ◽  
Usual Intake ◽  
Study Population ◽  
Using Data ◽  
Parameter Distributions

AbstractThere are statistical methods that remove the within-person random error and estimate the usual intake when there is a second 24-h recall (24HR) for at least a subsample of the study population. We aimed to compare the distribution of usual food intake estimated by statistical models with the distribution of observed usual intake. A total of 302 individuals from Rio de Janeiro (Brazil) answered twenty, non-consecutive 24HR; the average length of follow-up was 3 months. The usual food intake was considered as the average of the 20 collection days of food intake. Using data sets with a pair of 2 collection days, usual percentiles of intake of the selected foods using two methods were estimated (National Cancer Institute (NCI) method and Multiple Source Method (MSM)). These estimates were compared with the percentiles of the observed usual intake. Selected foods comprised a range of parameter distributions: skewness, percentage of zero intakes and within- and between-person intakes. Both methods performed well but failed in some situations. In most cases, NCI and MSM produced similar percentiles between each other and values very close to the true intake, and they better represented the usual intake compared with 2-d mean. The smallest precision was observed in the upper tail of the distribution. In spite of the underestimation and overestimation of percentiles of intake, from a public health standpoint, these biases appear not to be of major concern.

Estimating spawning escapements from periodic counts: a comparison of methods

1999 ◽  
Vol 56 (5) ◽  
pp. 888-896 ◽  
Author(s):  
Ray Hilborn ◽  
Brian G Bue ◽  
Samuel Sharr
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Pacific Salmon ◽  
Interpolation Method ◽  
Linear Interpolation ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Data Sets ◽  
Time Model ◽  
Using Data

The escapement of Pacific salmon is often estimated by periodic counts of spawners, calculating the number of fish-days present and dividing by the average number of days a fish spends in the survey area. We present a maximum likelihood method to calculate the number of spawning fish and compare this approach with the most commonly used method, which relies on linear interpolation between observations. The maximum likelihood method is computationally more demanding; however, it does provide a statistical basis for describing uncertainty and can also be used to deal with data sets where the first or last counts are nonzero or where there are few observations. We compared escapement estimation methods using data from 18 experimental streams where the number of fish in the stream was evaluated by weir and carcass counts. In this comparison, the method of linear interpolation deviated from the weir count by an average of 19%, whereas the maximum likelihood method deviated by 23, 24, 30, or 40% depending upon which likelihood and arrival time model was used. We conclude that for most data sets where measures of uncertainty are not required, the linear interpolation method is adequate but recommend an examination of maximum likelihood methods when an estimate of uncertainty is required.

Assessment of Regional Explosion Discriminants Using Data Sets of Unparalleled Spatial Sampling

2012 ◽  
Author(s):  
Kate C. Miller ◽  
Lindsay L. Worthington ◽  
Steven Harder ◽  
Scott Phillips ◽  
Hans Hartse ◽  
...  
Keyword(s):  
Spatial Sampling ◽  
Data Sets ◽  
Using Data

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals Transforming variables to central normality

2021 ◽  
Author(s):  
Jakob Raymaekers ◽  
Peter J. Rousseeuw
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Simulation Study ◽  
Real Data ◽  
Data Sets ◽  
Transformation Parameter ◽  
Likelihood Estimator ◽  
Extensive Simulation ◽  
Highly Sensitive

AbstractMany real data sets contain numerical features (variables) whose distribution is far from normal (Gaussian). Instead, their distribution is often skewed. In order to handle such data it is customary to preprocess the variables to make them more normal. The Box–Cox and Yeo–Johnson transformations are well-known tools for this. However, the standard maximum likelihood estimator of their transformation parameter is highly sensitive to outliers, and will often try to move outliers inward at the expense of the normality of the central part of the data. We propose a modification of these transformations as well as an estimator of the transformation parameter that is robust to outliers, so the transformed data can be approximately normal in the center and a few outliers may deviate from it. It compares favorably to existing techniques in an extensive simulation study and on real data.

scholarly journals New class of Lindley distributions: properties and applications

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Duha Hamed ◽  
Ahmad Alzaghal
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Simulation Study ◽  
Likelihood Estimation ◽  
Statistical Properties ◽  
Data Sets ◽  
Lifetime Data ◽  
Unknown Parameters ◽  
Lindley Distribution ◽  
New Class

AbstractA new generalized class of Lindley distribution is introduced in this paper. This new class is called the T-Lindley{Y} class of distributions, and it is generated by using the quantile functions of uniform, exponential, Weibull, log-logistic, logistic and Cauchy distributions. The statistical properties including the modes, moments and Shannon’s entropy are discussed. Three new generalized Lindley distributions are investigated in more details. For estimating the unknown parameters, the maximum likelihood estimation has been used and a simulation study was carried out. Lastly, the usefulness of this new proposed class in fitting lifetime data is illustrated using four different data sets. In the application section, the strength of members of the T-Lindley{Y} class in modeling both unimodal as well as bimodal data sets is presented. A member of the T-Lindley{Y} class of distributions outperformed other known distributions in modeling unimodal and bimodal lifetime data sets.

POSTPARTUM AMENORRHOEA IN RURAL EASTERN UTTAR PRADESH, INDIA

1998 ◽  
Vol 30 (2) ◽  
pp. 227-243
Author(s):  
K. N. S. YADAVA ◽  
S. K. JAIN
Keyword(s):  
Higher Education ◽  
Uttar Pradesh ◽  
Current Status Data ◽  
North India ◽  
Current Status ◽  
Data Sets ◽  
Survival Status ◽  
The Mean ◽  
The Difference ◽  
Using Data

This paper calculates the mean duration of the postpartum amenorrhoea (PPA) and examines its demographic, and socioeconomic correlates in rural north India, using data collected through 'retrospective' (last but one child) as well as 'current status' (last child) reporting of the duration of PPA.The mean duration of PPA was higher in the current status than in the retrospective data;n the difference being statistically significant. However, for the same mothers who gave PPA information in both the data sets, the difference in mean duration of PPA was not statistically significant. The correlates were identical in both the data sets. The current status data were more complete in terms of the coverage, and perhaps less distorted by reporting errors caused by recall lapse.A positive relationship of the mean duration of PPA was found with longer breast-feeding, higher parity and age of mother at the birth of the child, and the survival status of the child. An inverse relationship was found with higher education of a woman, higher education of her husband and higher socioeconomic status of her household, these variables possibly acting as proxies for women's better nutritional status.

scholarly journals Using Machine Learning Algorithms on data residing in SAP ERP Application to predict equipment failures

2018 ◽  
Vol 7 (2.28) ◽  
pp. 312
Author(s):  
Manu Kohli
Keyword(s):  
Data Extraction ◽  
Indirect Costs ◽  
True Positive Rate ◽  
Spare Parts ◽  
Machine Learning Algorithms ◽  
Machine Language ◽  
Support Vector ◽  
Data Sets ◽  
Equipment Failure ◽  
Using Data

Asset intensive Organizations have searched long for a framework model that would timely predict equipment failure. Timely prediction of equipment failure substantially reduces direct and indirect costs, unexpected equipment shut-downs, accidents, and unwarranted emission risk. In this paper, the author proposes a model that can predict equipment failure by using data from SAP Plant Maintenance module. To achieve that author has applied data extraction algorithm and numerous data manipulations to prepare a classification data model consisting of maintenance records parameters such as spare parts usage, time elapsed since last completed maintenance and the period to the next scheduled maintained and so on. By using unsupervised learning technique of clustering, the author observed a class to cluster evaluation of 80% accuracy. After that classifier model was trained using various machine language (ML) algorithms and subsequently tested on mutually exclusive data sets with an objective to predict equipment breakdown. The classifier model using ML algorithms such as Support Vector Machine (SVM) and Decision Tree (DT) returned an accuracy and true positive rate (TPR) of greater than 95% to predict equipment failure. The proposed model acts as an Advanced Intelligent Control system contributing to the Cyber-Physical Systems for asset intensive organizations. 

Relationship Between Environmental Temperature and Yields of Subarctic and Temperate Zone Fish Species

1983 ◽  
Vol 40 (10) ◽  
pp. 1829-1837 ◽  
Author(s):  
David A. Schlesinger ◽  
Henry A. Regier
Keyword(s):  
Optimal Growth ◽  
Temperate Zone ◽  
Data Sets ◽  
Coregonus Clupeaformis ◽  
Influence Of Temperature On ◽  
Northern United States ◽  
Using Data ◽  
Curvilinear Relationships ◽  
Fish Yields

Fishes inhabiting subarctic and temperate zone lakes exhibit distinct optimal growth temperatures and temperature preferenda. However, within regional data sets, attempts to correlate fish yields with temperature variables have generally been unsuccessful. In our study, curvilinear relationships between "long-term mean annual air temperature" (TEMP) and sustained yields of three species were fitted using data from 23 intensively fished lakes in Canada and the northern United States. Optimum TEMP values for sustained yield were approximately −1.0, 1.5, and 2 °C, respectively, for lake whitefish (Coregonus clupeaformis), northern pike (Esox lucius), and walleye (Stizostedion vitreum vitreum). These differences suggest that the influence of temperature on sustained fish yields from subarctic and temperate zone lakes may, in the past, have been underestimated.

Comparison of odontocete populations of the Marquesas and Society Islands (French Polynesia)

2008 ◽  
Vol 89 (5) ◽  
pp. 931-941 ◽  
Author(s):  
Alexandre Gannier
Keyword(s):  
Relative Abundance ◽  
Bottlenose Dolphin ◽  
Bottlenose Dolphins ◽  
Sperm Whale ◽  
French Polynesia ◽  
Society Islands ◽  
Spotted Dolphin ◽  
Abundance Indices ◽  
Beaked Whales ◽  
Using Data

Small boat surveys were organized to study cetaceans of the Marquesas (9°S and 140°W) and the Society Islands (17°S and 150°W) in French Polynesia. Prospecting took place from 12–15 m sailboats, between 1996 and 2001 with systematic visual searching. Boats moved according to sea conditions, at a mean speed of 10 km/h. Effective effort of 4856 km in the Marquesas and 10,127 km in the Societies were logged. Relative abundance indices were processed for odontocetes using data obtained with Beaufort 4 or less. In the Marquesas, 153 on-effort sightings were obtained on 10 delphinids species including the spotted dolphin, spinner dolphin, bottlenose dolphin, melon-headed whale and rough-toothed dolphin. In the Societies, 153 sightings of 12 odontocetes included delphinids (spinner, rough-toothed and bottlenose dolphins, short-finned pilot and melon-headed whales, Fraser's dolphin, Risso's dolphin and pygmy killer whale) and two species of beaked whales, the sperm whale and dwarf sperm whale. Relative abundance indices were higher in the Marquesas than in the Societies both inshore (0.93 ind/km2 against 0.36 ind/km2) and offshore (0.28 ind/km2 against 0.14 ind/km2). Differences in remote-sensed primary production were equally important, the Marquesas waters featuring an annual average of 409 mgC.m−2 · day−1 and the Societies of only 171 mgC · m−2 · day−1. The presence of a narrow shelf around the Marquesas also accounted for differences in odontocete populations, in particular the delphinids.

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