scholarly journals Modelling and Computation Using NCoRM Mixtures for Density Regression

2018 ◽  
Vol 13 (3) ◽  
pp. 897-916 ◽  
Author(s):  
Jim Griffin ◽  
Fabrizio Leisen
Keyword(s):  
Density Regression

scholarly journals Deeply-supervised density regression for automatic cell counting in microscopy images

2021 ◽  
Vol 68 ◽  
pp. 101892
Author(s):  
Shenghua He ◽  
Kyaw Thu Minn ◽  
Lilianna Solnica-Krezel ◽  
Mark A. Anastasio ◽  
Hua Li
Keyword(s):  
Cell Counting ◽  
Density Regression ◽  
Microscopy Images

scholarly journals Adaptive Bayesian density regression for high-dimensional data

Bernoulli ◽  
2016 ◽  
Vol 22 (1) ◽  
pp. 396-420 ◽  
Author(s):  
Weining Shen ◽  
Subhashis Ghosal
Keyword(s):  
High Dimensional Data ◽  
High Dimensional ◽  
Density Regression

Territory Size as a Predictor of the Upper Limit to Population Density of Juvenile Salmonids in Streams

1990 ◽  
Vol 47 (9) ◽  
pp. 1724-1737 ◽  
Author(s):  
James W. A. Grant ◽  
Donald L. Kramer
Keyword(s):  
Population Density ◽  
Experimental Studies ◽  
Strong Support ◽  
Fork Length ◽  
Maximum Density ◽  
Territory Size ◽  
Published Data ◽  
Density Dependent ◽  
Size Limits ◽  
Density Regression

We examined the old, but untested hypothesis that territory size limits the maximum population density of salmonids in streams. We used published data to derive an interspecific regression of territory size (m2) on fork length (cm) (log10 territory size = 2.61 log10 length—2.83, r2 = 0.87, n = 23). Growth and mortality trajectories of salmonid cohorts from eight experimental studies were compared to the maximum-density regression, the inverse of the territory–size regression. In shallow habitats, such as riffles and raceways, the cohort trajectories followed the maximum density regression quite closely and were consistent with the territory–size hypothesis. In addition, natural densities in eight other studies did not exceed the predicted maximum density and tended to fail within the 95% C.L. of the maximum-density regression. Data from shallow habitats, therefore, provide strong support for the territory–size hypothesis. A linear logistic response model showed that the probability of observing density-dependent growth, mortality, or emigration increased significantly with increasing values of an index of habitat saturation, developed from the territory–size regression. Our results suggest that the territory–size regression has practical value for predicting the maximum densities of stream-dwelling salmonids in shallow habitats and the occurrence of density-dependent population responses.

LABORATORY STUDIES OF PREDATION OF SNAILS BY LARVAE OF THE MARSH FLY, SEPEDON TENUICORNIS (DIPTERA: SCIOMYZIDAE)

1971 ◽  
Vol 103 (5) ◽  
pp. 638-649 ◽  
Author(s):  
Robert P. Geckler
Keyword(s):  
Regression Line ◽  
Laboratory Studies ◽  
Snail Density ◽  
Distance Data ◽  
Time Required ◽  
Image Position ◽  
Density Regression

AbstractLarvae of the marsh fly, Sepedon tenuicornis Cresson, were used to determine (1) number and approximate volume of snails killed before pupation, (2) time required to make the first kill as a function of distance and snail density, and (3) vulnerability of snails of different sizes to larvae of different sizes.Results1. Mean number of snails killed (by 34 larvae): 8.1 ± 2.5, (S.E.) Volume: 544 ± 256 mm3.2. Regression line relating time required to kill as a function of snail density: T (minutes) = 65.3–111.6 × (snail density).Regression line relating time to kill and snail densities of 0.6 and 1.2 snails/cm as a function of distance from snails: T = 0.79–0.33 × (distance).Data from a snail density of 0.3/cm could not be pooled because of high day-to-day variation in larvae.3. Vulnerability of snails can be expressed for each larva length by the equation: Fraction of snails killed = a + b × (snail size).Values of a and b are as follows:

scholarly journals Density Regression Based on Proportional Hazards Family

Entropy ◽  
2015 ◽  
Vol 17 (6) ◽  
pp. 3679-3691
Author(s):  
Wei Dang ◽  
Keming Yu
Keyword(s):  
Proportional Hazards ◽  
Density Regression

RESPONSE SURFACE METHODOLOGY: A TOOL FOR ASSESSING THE ROLE OF COMPOUNDING INGREDIENTS IN PEROXIDE VULCANIZATION OF NATURAL RUBBER

2016 ◽  
Vol 89 (2) ◽  
pp. 211-226 ◽  
Author(s):  
Rejitha Rajan ◽  
Siby Varghese ◽  
Meera Balachandran ◽  
K. E. George
Keyword(s):  
Zinc Oxide ◽  
Response Surface Methodology ◽  
Natural Rubber ◽  
Response Surface ◽  
Response Surfaces ◽  
Regression Equations ◽  
Face Centered ◽  
The Relationship ◽  
Density Regression

ABSTRACT Response surface methodology was used for assessing the role of various compounding ingredients, including zinc oxide, antioxidant, coagent, oil, and filler, in peroxide vulcanization of natural rubber. A face-centered central composite design with four factors at three different levels was used to obtain the relationship between vulcanizate properties and the level of ingredients. The four factors selected were filler and oil ratio and the contents of zinc oxide, antioxidant, and coagent. The filler and oil ratio was kept constant throughout the experiment. The vulcanizates were evaluated for their mechanical properties: tensile strength, elongation, modulus (M100), tear strength, hardness, compression set (70 and 100 °C), and crosslink density. Regression equations were generated to model the properties of interest, and response surfaces and contour diagrams were plotted.

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