2012: Vol. 36, Núm. 2
http://hdl.handle.net/2099/13227
2022-05-26T12:28:17ZFlexible geoadditive survival analysis of non-Hodgkin lymphoma in Peru
http://hdl.handle.net/2099/13325
Flexible geoadditive survival analysis of non-Hodgkin lymphoma in Peru
Flores, Carlos; Rodríguez-Girondo, Mar; Cadarso-Suárez, Carmen; Kneib, Thomas; Gómez Melis, Guadalupe; Casanova, Luís
Knowledge of prognostic factors is an important task for the clinical management of Non Hodgkin
Lymphoma (NHL). In this work, we study the variables affecting survival of NHL in Peru by means
of geoadditive Cox-type structured hazard regression models while accounting for potential spatial
correlations in the survival times. We identified eight covariates with significant effect for overall
survival. Some of them are widely known such as age, performance status, clinical stage and lactic
dehydrogenase, but we also identified hemoglobin, leukocytes and lymphocytes as covariates with
a significant effect on the overall survival of patients with NHL. Besides, the effect of continuous
covariates is clearly nonlinear and hence impossible to detect with the classical Cox method.
Although the spatial component does not show a significant effect, the results show a trend of low
risk in certain areas
2013-05-21T15:13:36ZFlores, CarlosRodríguez-Girondo, MarCadarso-Suárez, CarmenKneib, ThomasGómez Melis, GuadalupeCasanova, LuísKnowledge of prognostic factors is an important task for the clinical management of Non Hodgkin
Lymphoma (NHL). In this work, we study the variables affecting survival of NHL in Peru by means
of geoadditive Cox-type structured hazard regression models while accounting for potential spatial
correlations in the survival times. We identified eight covariates with significant effect for overall
survival. Some of them are widely known such as age, performance status, clinical stage and lactic
dehydrogenase, but we also identified hemoglobin, leukocytes and lymphocytes as covariates with
a significant effect on the overall survival of patients with NHL. Besides, the effect of continuous
covariates is clearly nonlinear and hence impossible to detect with the classical Cox method.
Although the spatial component does not show a significant effect, the results show a trend of low
risk in certain areasHurdle negative binomial regression model with right censored count data
http://hdl.handle.net/2099/13324
Hurdle negative binomial regression model with right censored count data
Saffari, Seyed Ehsan; Adnan, Robiah; Greene, William
A Poisson model typically is assumed for count data. In many cases because of many zeros in
the response variable, the mean is not equal to the variance value of the dependent variable.
Therefore, the Poisson model is no longer suitable for this kind of data. Thus, we suggest
using a hurdle negative binomial regression model to overcome the problem of overdispersion.
Furthermore, the response variable in such cases is censored for some values. In this paper,
a censored hurdle negative binomial regression model is introduced on count data with many
zeros. The estimation of regression parameters using maximum likelihood is discussed and the
goodness-of-fit for the regression model is examined
2013-05-21T15:11:08ZSaffari, Seyed EhsanAdnan, RobiahGreene, WilliamA Poisson model typically is assumed for count data. In many cases because of many zeros in
the response variable, the mean is not equal to the variance value of the dependent variable.
Therefore, the Poisson model is no longer suitable for this kind of data. Thus, we suggest
using a hurdle negative binomial regression model to overcome the problem of overdispersion.
Furthermore, the response variable in such cases is censored for some values. In this paper,
a censored hurdle negative binomial regression model is introduced on count data with many
zeros. The estimation of regression parameters using maximum likelihood is discussed and the
goodness-of-fit for the regression model is examinedStatistical modelling and forecasting of outstanding liabilities in non-life insurance
http://hdl.handle.net/2099/13323
Statistical modelling and forecasting of outstanding liabilities in non-life insurance
Martínez-Miranda, María Dolores; Nielsen, Jens Perch; Wüthrich, Mario V.
Non-life insurance companies need to build reserves to meet their claims liability cash flows. They
often work with aggregated data. Recently it has been suggested that better statistical properties
can be obtained when more aggregated data are available for statistical analysis than just the
classical aggregated payments. When also the aggregated number of claims is available one can
define a full statistical model of the nature of the number of claims, their delay until payment and
the nature of these payments. In this paper we provide a new development in this direction by
entering yet another set of aggregated data, namely the number of payments and when they
occurred. A new element of our statistical analysis is that we are able to incorporate inflationary
trends of payments in a direct and explicit way. Our new method is illustrated on a real life data set
2013-05-21T15:06:49ZMartínez-Miranda, María DoloresNielsen, Jens PerchWüthrich, Mario V.Non-life insurance companies need to build reserves to meet their claims liability cash flows. They
often work with aggregated data. Recently it has been suggested that better statistical properties
can be obtained when more aggregated data are available for statistical analysis than just the
classical aggregated payments. When also the aggregated number of claims is available one can
define a full statistical model of the nature of the number of claims, their delay until payment and
the nature of these payments. In this paper we provide a new development in this direction by
entering yet another set of aggregated data, namely the number of payments and when they
occurred. A new element of our statistical analysis is that we are able to incorporate inflationary
trends of payments in a direct and explicit way. Our new method is illustrated on a real life data setThe new class of Kummer beta generalized distributions
http://hdl.handle.net/2099/13318
The new class of Kummer beta generalized distributions
Pescim, R. R.; Cordeiro, G. M.; Demétrio, Clarice G.B.; Ortega, E. M. M.; Nadarajah, S.
Ng and Kotz (1995) introduced a distribution that provides g
reater flexibility to extremes. We define
and study a new class of distributions called the Kummer beta
generalized family to extend the
normal, Weibull, gamma and Gumbel distributions, among sev
eral other well-known distributions.
Some special models are discussed. The ordinary moments of a
ny distribution in the new family
can be expressed as linear functions of probability weighte
d moments of the baseline distribution.
We examine the asymptotic distributions of the extreme valu
es. We derive the density function
of the order statistics, mean absolute deviations and entro
pies. We use maximum likelihood
estimation to fit the distributions in the new class and illus
trate its potentiality with an application
to a real data set
2013-05-16T16:43:54ZPescim, R. R.Cordeiro, G. M.Demétrio, Clarice G.B.Ortega, E. M. M.Nadarajah, S.Ng and Kotz (1995) introduced a distribution that provides g
reater flexibility to extremes. We define
and study a new class of distributions called the Kummer beta
generalized family to extend the
normal, Weibull, gamma and Gumbel distributions, among sev
eral other well-known distributions.
Some special models are discussed. The ordinary moments of a
ny distribution in the new family
can be expressed as linear functions of probability weighte
d moments of the baseline distribution.
We examine the asymptotic distributions of the extreme valu
es. We derive the density function
of the order statistics, mean absolute deviations and entro
pies. We use maximum likelihood
estimation to fit the distributions in the new class and illus
trate its potentiality with an application
to a real data setA note on the use of supply-use tables in impact analyses
http://hdl.handle.net/2099/13317
A note on the use of supply-use tables in impact analyses
Lenzen, Manfred; Rueda-Cantuche, José M.
Little attention has so far been paid to the problems inheren
t in interpreting the meaning of results
from standard impact analyses using symmetric input-outpu
t tables. Impacts as well as drivers
of these impacts must be either of the product type or of the in
dustry type. Interestingly, since
supply-use tables distinguish products and industries, th
ey can cope with product impacts driven
by changes in industries, and vice versa. This paper contrib
utes in two ways. Firstly, the demand-
driven Leontief quantity model, both for industry-by-indu
stry as well as for product-by-product
tables, is formalised on the basis of supply-use tables, thu
s leading to impact multipliers, both for
industries and products. Secondly, we demonstrate how the s
upply-use formulation can improve
the incorporation of disparate satellite data into input-o
utput models, by offering both industry and
product representation. Supply-use blocks can accept any m
ix of industry and product satellite
data, as long as these are not overlapping
2013-05-16T16:42:27ZLenzen, ManfredRueda-Cantuche, José M.Little attention has so far been paid to the problems inheren
t in interpreting the meaning of results
from standard impact analyses using symmetric input-outpu
t tables. Impacts as well as drivers
of these impacts must be either of the product type or of the in
dustry type. Interestingly, since
supply-use tables distinguish products and industries, th
ey can cope with product impacts driven
by changes in industries, and vice versa. This paper contrib
utes in two ways. Firstly, the demand-
driven Leontief quantity model, both for industry-by-indu
stry as well as for product-by-product
tables, is formalised on the basis of supply-use tables, thu
s leading to impact multipliers, both for
industries and products. Secondly, we demonstrate how the s
upply-use formulation can improve
the incorporation of disparate satellite data into input-o
utput models, by offering both industry and
product representation. Supply-use blocks can accept any m
ix of industry and product satellite
data, as long as these are not overlappingOn developing ridge regression parameters: a graphical investigation
http://hdl.handle.net/2099/13316
On developing ridge regression parameters: a graphical investigation
Muniz, Gisela; Golam Kibria, B. M.; Mansson, Kristofer Mansson; Shukur, Ghazi
In this paper we review some existing and propose some new est
imators for estimating the ridge
parameter. All in all 19 different estimators have been stud
ied. The investigation has been carried
out using Monte Carlo simulations. A large number of differe
nt models have been investigated
where the variance of the random error, the number of variabl
es included in the model, the
correlations among the explanatory variables, the sample s
ize and the unknown coefficient vector
were varied. For each model we have performed 2000 replicati
ons and presented the results both
in term of figures and tables. Based on the simulation study, w
e found that increasing the number
of correlated variable, the variance of the random error and
increasing the correlation between
the independent variables have negative effect on the mean s
quared error. When the sample size
increases the mean squared error decreases even when the cor
relation between the independent
variables and the variance of the random error are large. In a
ll situations, the proposed estimators
have smaller mean squared error than the ordinary least squa
res and other existing estimators
2013-05-16T16:41:13ZMuniz, GiselaGolam Kibria, B. M.Mansson, Kristofer ManssonShukur, GhaziIn this paper we review some existing and propose some new est
imators for estimating the ridge
parameter. All in all 19 different estimators have been stud
ied. The investigation has been carried
out using Monte Carlo simulations. A large number of differe
nt models have been investigated
where the variance of the random error, the number of variabl
es included in the model, the
correlations among the explanatory variables, the sample s
ize and the unknown coefficient vector
were varied. For each model we have performed 2000 replicati
ons and presented the results both
in term of figures and tables. Based on the simulation study, w
e found that increasing the number
of correlated variable, the variance of the random error and
increasing the correlation between
the independent variables have negative effect on the mean s
quared error. When the sample size
increases the mean squared error decreases even when the cor
relation between the independent
variables and the variance of the random error are large. In a
ll situations, the proposed estimators
have smaller mean squared error than the ordinary least squa
res and other existing estimators