search for: concav

Displaying 9 results from an estimated 9 matches for "concav".

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2003 Jul 18
1
Grandstream BudgeTone 102 initial experiences
...anged the name or labeling from BudgeTone to ExpensiTone but kept the good pricing that would help a lot. It lacks elegance. The soon to be released grey model may improve it's image. You cannot wall mount this phone (easily). There are mounting holes on the bottom and the handset has a little concavity in the right place but there's no nub/pin to keep the handset in place when on hook. I haven't asked the Grandstream folks about this--maybe I got a dud or maybe they already plan to address this. I just bought a new house and am seriously thinking about sprinkling these throughout...
2008 Sep 16
0
Maximum likelihood estimation of a truncated regression model
...function to estimate truncated regression models as does STATA, LIMDEP etc. I tried the survival and FEAR packages and couldn't fit it for my case. So I wrote the log likelihood function of the truncated regression model and reparametrised it using Olsen (1978) so that the function is globally concave and has an unique maximiser. I used a quasi-Newton method (BFGS) to maximise my function. I also used Newton-Raphson method (maxNR) to maximise my function. The (naive) code can be seen below. sw1<-function(theta,dhat,z) { gamma<-theta[1:2] eta<-theta[3] d1<-dhat*eta-z%*%gamma d2<-...
2004 Jan 20
0
..You can be a ~Se-xxMachine~
adventurous burgundian rook elkhart butterball concave chaplin dustbin trout plane pedestal cinquefoil ely indigene choir curvilinear scald zing bramble braid cobol composition tripartite bust torpedo transverse marvin crestfallen troika molybdate aflame righteous aden diffusive lenin wilcox downright pittsburgh inflammable smear cathy orinoco
2012 Mar 07
0
sparsenet: a new package for sparse model selection
...en L1 and L0 regularization. One nice feature of this family is that the single-coordinate optimization problems are convex, making it ideal for coordinate descent. The package fits the regularization surface for each parameter - a surface over the two-dimensional space of tuning parameters. The concavity parameter gamma indexes the member of the family, and lambda is the usual Lagrange penalty parameter which determines the strength of the penalty. Sparsenet is extremely fast. For example, with 10K variables and 1K samples, the entire surface with 10 values of gamma and 50 values of lambda tak...
2012 Mar 07
0
sparsenet: a new package for sparse model selection
...en L1 and L0 regularization. One nice feature of this family is that the single-coordinate optimization problems are convex, making it ideal for coordinate descent. The package fits the regularization surface for each parameter - a surface over the two-dimensional space of tuning parameters. The concavity parameter gamma indexes the member of the family, and lambda is the usual Lagrange penalty parameter which determines the strength of the penalty. Sparsenet is extremely fast. For example, with 10K variables and 1K samples, the entire surface with 10 values of gamma and 50 values of lambda tak...
2007 Sep 16
5
using tc to drop packets based on the diffserc or tos value
Hi all, I am wondering if anyone can help me to resolve a problem. I am trying to use tc command in linux to drop udp packets of specific diffserv value. I am able set diffserv value successfully in the udp packet using command:- [root@scotch src]#iptables --table mangle --append OUTPUT \ --out-interface eth0 --protocol udp --source-port 5060 \ --jump DSCP --set-dscp 8 but i am not able to
2001 Aug 01
1
glm() with non-integer responses
A question about the inner workings of glm() and dpois(): Suppose I call glm(y ~ x, family=poisson, weights = w) where y contains NON-INTEGER (but still nonnegative) values. (a) Does glm() still correctly maximise the weighted Poisson loglikelihood ? (i.e. the function given by the same formal expression as the weighted loglikelihood of independent Poisson variables Y_i except that the
2005 Jun 10
0
Replies of the question about robustness of segmented regression
...to Muggeo: In addition to valuable Achim's comments. As Achim said, you can try different starting values to assess how the final solution depends on them. Then select one having the best logLik (or the minimum RSS). Everybody dealing with nonlinear models knows that the logLik may be not concave. This is particulary true for broken-line model, so different starting values (psi0) sometimes can lead to different solutions (segmented performs "just" an iterative estimating algorithm..). This sensitivity depends on your data: the more clear-cut the relationship, the stabler the...
2002 May 24
5
intersecting polygons and conversion from decimal degree to km
Dear all, 1. How can I compute the intersecting area between 2 polygons ? 2. I have polygons with coordinates in decimal degrees (i.e. 13 deg 30 min = 13.5 decimal degrees). I want to compute their area and get the results in square meters or square kiometers. Can anyone give me a conversion coefficient or a pointer where I can find this information (sorry for this off topic question) ? Thanks