Schiewe, M C; Fitz, T A; Brown, J L; Stuart, L D; Wildt, D E
1991-09-01
Ewes were treated with exogenous follicle-stimulating hormone (FSH) and oestrus was synchronized using either a dual prostaglandin F-2 alpha (PGF-2 alpha) injection regimen or pessaries impregnated with medroxy progesterone acetate (MAP). Natural cycling ewes served as controls. After oestrus or AI (Day 0), corpora lutea (CL) were enucleated surgically from the left and right ovaries on Days 3 and 6, respectively. The incidence of premature luteolysis was related (P less than 0.05) to PGF-2 alpha treatment and occurred in 7 of 8 ewes compared with 0 of 4 controls and 1 of 8 MAP-exposed females. Sheep with regressing CL had lower circulating and intraluteal progesterone concentrations and fewer total and small dissociated luteal cells on Day 3 than gonadotrophin-treated counterparts with normal CL. Progesterone concentration in the serum and luteal tissue was higher (P less than 0.05) in gonadotrophin-treated ewes with normal CL than in the controls; but luteinizing hormone (LH) receptors/cell were not different on Days 3 and 6. There were no apparent differences in the temporal patterns of circulating oestradiol-17 beta, FSH and LH. High progesterone in gonadotrophin-treated ewes with normal CL coincided with an increase in total luteal mass and numbers of cells, which were primarily reflected in more small luteal cells than in control ewes. Gonadotrophin-treated ewes with regressing CL on Day 3 tended (P less than 0.10) to have fewer small luteal cells and fewer (P less than 0.05) low-affinity PGF-2 alpha binding sites than sheep with normal CL. By Day 6, luteal integrity and cell viability was absent in ewes with prematurely regressed CL. These data demonstrate that (i) the incidence of premature luteal regression is highly correlated with the use of PGF-2 alpha; (ii) this abnormal luteal tissue is functionally competent for 2-3 days after ovulation, but deteriorates rapidly thereafter and (iii) luteal-dysfunctioning ewes experience a reduction in numbers of
Huang, Xuan; Chen, Li; Xia, You-Bing; Xie, Min; Sun, Qin; Yao, Bing
2018-03-15
Electroacupuncture (EA) is an effective and safe therapeutic method widely used for treating clinical diseases. Previously, we found that EA could decrease serum hormones and reduce ovarian size in ovarian hyperstimulation syndrome (OHSS) rat model. Nevertheless, the mechanisms that contribute to these improvements remain unclear. HE staining was used to count the number of corpora lutea (CL) and follicles. Immunohistochemical and ELISA were applied to examine luteal functional and structural regression. Immunoprecipitation was used for analyzing the interaction between NPY (neuropeptide Y) and COX-2; western blotting and qRT-PCR were used to evaluate the expressions of steroidogenic enzymes and PKA/CREB pathway. EA treatment significantly reduced the ovarian weight and the number of CL, also decreased ovarian and serum levels of PGE2 and COX-2 expression; increased ovarian PGF2α levels and PGF2α/PGE2 ratio; decreased PCNA expression and distribution; and increased cyclin regulatory inhibitor p27 expression to have further effect on the luteal formation, and promote luteal functional and structural regression. Moreover, expression of COX-2 in ovaries was possessed interactivity increased expression of NPY. Furthermore, EA treatment lowered the serum hormone levels, inhibited PKA/CREB pathway and decreased the expressions of steroidogenic enzymes. Hence, interaction with COX-2, NPY may affect the levels of PGF2α and PGE2 as well as impact the proliferation of granulosa cells in ovaries, thus further reducing the luteal formation, and promoting luteal structural and functional regression, as well as the ovarian steroidogenesis following EA treatment. EA treatment could be an option for preventing OHSS in ART. Copyright © 2018 Elsevier Inc. All rights reserved.
Fang, Ling; Gu, Caiyun; Liu, Xinyu; Xie, Jiabin; Hou, Zhiguo; Tian, Meng; Yin, Jia; Li, Aizhu; Li, Yubo
2017-01-01
Primary dysmenorrhea (PD) is a common gynecological disorder which, while not life-threatening, severely affects the quality of life of women. Most patients with PD suffer ovarian hormone imbalances caused by uterine contraction, which results in dysmenorrhea. PD patients may also suffer from increases in estrogen levels caused by increased levels of prostaglandin synthesis and release during luteal regression and early menstruation. Although PD pathogenesis has been previously reported on, these studies only examined the menstrual period and neglected the importance of the luteal regression stage. Therefore, the present study used urine metabolomics to examine changes in endogenous substances and detect urine biomarkers for PD during luteal regression. Ultra performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry was used to create metabolomic profiles for 36 patients with PD and 27 healthy controls. Principal component analysis and partial least squares discriminate analysis were used to investigate the metabolic alterations associated with PD. Ten biomarkers for PD were identified, including ornithine, dihydrocortisol, histidine, citrulline, sphinganine, phytosphingosine, progesterone, 17-hydroxyprogesterone, androstenedione, and 15-keto-prostaglandin F2α. The specificity and sensitivity of these biomarkers was assessed based on the area under the curve of receiver operator characteristic curves, which can be used to distinguish patients with PD from healthy controls. These results provide novel targets for the treatment of PD. PMID:28098892
Del Canto, Felipe; Sierralta, Walter; Kohen, Paulina; Muñoz, Alex; Strauss, Jerome F; Devoto, Luigi
2007-11-01
The natural process of luteolysis and luteal regression is induced by withdrawal of gonadotropin support. The objectives of this study were: 1) to compare the functional changes and apoptotic features of natural human luteal regression and induced luteal regression; 2) to define the ultrastructural characteristics of the corpus luteum at the time of natural luteal regression and induced luteal regression; and 3) to examine the effect of human chorionic gonadotropin (hCG) on the steroidogenic response and apoptotic markers within the regressing corpus luteum. Twenty-three women with normal menstrual cycles undergoing tubal ligation donated corpus luteum at specific stages in the luteal phase. Some women received a GnRH antagonist prior to collection of corpus luteum, others received an injection of hCG with or without prior treatment with a GnRH antagonist. Main outcome measures were plasma hormone levels and analysis of excised luteal tissue for markers of apoptosis, histology, and ultrastructure. The progesterone and estradiol levels, corpus luteum DNA, and protein contents in induced luteal regression resembled those of natural luteal regression. hCG treatment raised progesterone and estradiol in both natural luteal regression and induced luteal regression. The increase in apoptosis detected in induced luteal regression by cytochrome c in the cytosol, activated caspase-3, and nuclear DNA fragmentation, was similar to that observed in natural luteal regression. The antiapoptotic protein Bcl-2 was significantly lower during natural luteal regression. The proapoptotic proteins Bax and Bak were at a constant level. Apoptotic and nonapoptotic death of luteal cells was observed in natural luteal regression and induced luteal regression at the ultrastructural level. hCG prevented apoptotic cell death, but not autophagy. The low number of apoptotic cells disclosed and the frequent autophagocytic suggest that multiple mechanisms are involved in cell death at luteal
Improving the luteal phase after ovarian stimulation
DEFF Research Database (Denmark)
Andersen, Claus Yding; Vilbour Andersen, K
2014-01-01
The human chorionic gonadotrophin (HCG) trigger used for final follicular maturation in connection with assisted reproduction treatment combines ovulation induction and early luteal-phase stimulation of the corpora lutea. The use of a gonadotrophin-releasing hormone agonist (GnRHa) for final...... follicular maturation has, however, for the first time allowed a separation of the ovulatory signal from the early luteal-phase support. This has generated new information that may improve the currently employed luteal-phase support. Thus, combined results from a number of randomized controlled trials using...
Ruptured corpus luteal cyst: Prediction of clinical outcomes with CT
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Lee, Myoung Seok; Moon, Min Hoan; Woo, Hyun Sik; Sung, Chang Kyu; Jeon, Hye Won; Lee, Taek Sang [SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul (Korea, Republic of)
2017-08-01
To evaluate the determinant pretreatment CT findings that can predict surgical intervention for patients suffering from corpus luteal cyst rupture with hemoperitoneum. From January 2009 to December 2014, a total of 106 female patients (mean age, 26.1 years; range, 17–44 years) who visited the emergency room of our institute for acute abdominal pain and were subsequently diagnosed with ruptured corpus luteal cyst with hemoperitoneum were included in the retrospective study. The analysis of CT findings included cyst size, cyst shape, sentinel clot sign, ring of fire sign, hemoperitoneum depth, active bleeding in portal phase and attenuation of hemoperitoneum. The comparison of CT findings between the surgery and conservative management groups was performed with the Mann-Whitney U test or chi-square test. Logistic regression analysis was used to determine significant CT findings in predicting surgical intervention for a ruptured cyst. Comparative analysis revealed that the presence of active bleeding and the hemoperitoneum depth were significantly different between the surgery and conservative management groups and were confirmed as significant CT findings for predicting surgery, with adjusted odds ratio (ORs) of 3.773 and 1.318, respectively (p < 0.01). On the receiver-operating characteristic curve analysis for hemoperitoneum depth, the optimal cut-off value was 5.8 cm with 73.7% sensitivity and 58.6% specificity (Az = 0.711, p = 0.004). In cases with a hemoperitoneum depth > 5.8 cm and concurrent active bleeding, the OR for surgery increased to 5.786. The presence of active bleeding and the hemoperitoneum depth on a pretreatment CT scan can be predictive warning signs of surgery for a patient with a ruptured corpus luteal cyst with hemoperitoneum.
Luteal phase support for assisted reproduction cycles
Linden, M. van der; Buckingham, K.; Farquhar, C.; Kremer, J.A.M.; Metwally, M.
2015-01-01
BACKGROUND: Progesterone prepares the endometrium for pregnancy by stimulating proliferation in response to human chorionic gonadotropin(hCG) produced by the corpus luteum. This occurs in the luteal phase of the menstrual cycle. In assisted reproduction techniques(ART), progesterone and/or hCG
Luteal phase support for assisted reproduction cycles
Linden, M. Van der; Buckingham, K.; Farquhar, C.; Kremer, J.A.M.; Metwally, M.
2011-01-01
BACKGROUND: Progesterone prepares the endometrium for pregnancy by stimulating proliferation in response to human chorionic gonadotropin (hCG), which is produced by the corpus luteum. This occurs in the luteal phase of the menstrual cycle. In assisted reproduction techniques (ART) the progesterone
Endocrine and molecular control of luteal and placental function in dogs: a review.
Kowalewski, M P
2012-12-01
In the domestic dog (Canis familiaris), the corpus luteum (CL) is the only source of progesterone (P4) in non-pregnant and pregnant animals. The progesterone secretion profiles are almost identical in both conditions until the last third of the luteal phase when the gradual P4 decline turns into a steep drop in pregnant bitches, indicating the onset of parturition. Consequently, the length of the CL-phase in non-pregnant dogs exceeds the luteal lifespan in pregnant animals. The canine CL-function is regulated by many species-specific regulatory mechanisms, the most intriguing of which is the reported independence of gonadotropic support during the first third of dioestrus. Recently, PGE2 has been proposed as one of the most important luteotropic factors acting locally during this time, but afterwards prolactin (PRL) appears to be the main luteotropic factor. Luteal regression/luteolysis occurs, however, in spite of an increased gonadotropic support. Lately, by demonstrating the expression of PRL-receptor (PRLr), a new insight into possible regulatory mechanisms has indicated that the supply of P4 could be controlled upstream of the steroidogenic machinery at the level of PRLr expression and/or function, subsequently leading to the functional suppression of the steroidogenic machinery. An endogenous source of a luteolytic agent is apparently lacking, implicating the luteal regression in non-pregnant bitches as a passive, degenerative process even if the PGF2α-receptor is constitutively expressed in canine CL. This is in contrast to pregnant dogs in which prepartum luteolysis seems to be an active process of CL destruction by PGF2α of utero/placental origin targeting the luteal PGF2α-receptor. © 2012 Blackwell Verlag GmbH.
Hoffmann Bernd; Guscetti Franco; Boos Alois; Gram Aykut; Michel Erika; Kowalewski Mariusz P; Aslan Selim; Reichler Iris
2011-01-01
Abstract Background Endocrine mechanisms governing canine reproductive function remain still obscure. Progesterone (P4) of luteal origin is required for maintenance of pregnancy. Corpora lutea (CL) are gonadotrop-independent during the first third of dioestrus; afterwards prolactin (PRL) is the primary luteotropic factor. Interestingly, the increasing PRL levels are accompanied by decreasing P4 concentrations, thus luteal regression/luteolysis occurs in spite of an increased availability of g...
Isolation and functional aspects of free luteal cells
International Nuclear Information System (INIS)
Luborsky, J.L.; Berhrman, H.R.
1985-01-01
Methods of luteal cell isolation employ enzymatic treatment of luteal tissue with collagenase and deoxyribonuclease. Additional enzymes such as hyaluronidase or Pronase are also used in some instances. Isolated luteal cells retain the morphological characteristics of steroid secreting cells after isolation. They contain mitochondria, variable amounts of lipid droplets, and an extensive smooth endoplasmic reticulum. Isolated luteal cells have been used in numerous studies to examine the regulation of steriodogenesis by luteinizing hormone (LH). LH receptor binding studies were employed to quantitate specific properties of hormone-receptor interaction in relation to cellular function. Binding of [ 125 I]LH to bovine luteal cells and membranes was compared and it was concluded that the enzymatic treatment used to isolate cells did not change the LH receptor binding kinetics
Hommeida, Abdelrahim; Nakao, Toshihiko; Kubota, Hirokazu
2004-07-01
The objective of this study was to investigate the types and incidence of luteal sub-function in lactating cows after artificial insemination (AI) and their relationship with pregnancy, and to clarify the relationship between luteal function and parity, body condition score (BCS), milk yield, and dietary intake. In 19 cows, milk samples were collected daily from AI to confirmation of pregnancy. Milk progesterone concentrations were determined by EIA. Based on peak progesterone concentration and the day of onset of luteal phase, 15 of 30 progesterone profiles (50%) were normal, with progesterone concentration reaching 1.0 ng/ml within 5 days after insemination and > or =2.0 ng/ml thereafter. In addition, 6 (20%) were insufficient, (progesterone concentration remained 1.0 ng/ml for only 7 days), and one (3%) remained basal. Cows with a normal profile had a higher (P rate than those with an abnormal profile (87% versus 33%, respectively). The amount of progesterone secreted in milk after first AI, as indicated by progesterone area under curve (AUC), was negatively correlated with milk yield (r = -0.83, P rates and high milk production and increased dietary intake during breeding were associated with reduced progesterone concentrations.
Baerwald, Angela; Vanden Brink, Heidi; Hunter, Caitlin; Beuker, Denae; Lim, Hyun; Lee, Chel Hee; Chizen, Donna
2018-04-01
The aim of the study was to test the hypothesis that the development of luteal phase dominant follicles (LPDFs) as women age is associated with abnormal luteal function. Luteal and antral follicle diameter were quantified in ovulatory women of midreproductive age (MRA; 18-35 y; n = 9) and advanced reproductive age (ARA; 45-55 y; n = 16) every 1 to 3 days during one complete interovulatory interval. Blood was drawn at each visit and assayed for progesterone, estradiol, inhibin A, follicle-stimulating hormone, and luteinizing hormone. Luteal diameter and hormone profiles were compared within MRA and ARA women with versus without LPDFs. Luteal growth and regression profiles were similar in MRA women with typical versus no LPDFs (13.9, 14.8 mm; P > 0.1); however, luteal phase estradiol and progesterone were greater in MRA women with typical (91.1 ng/L, 8.81 μg/L) versus no (48.8 ng/L, 7.32 μg/L) LPDFs, respectively (LPDF effect, P < 0.1). In the ARA group, mean luteal diameter was lowest in women with atypical LPDFs (12.3 mm), greatest in those with typical LPDFs (16.0 mm), and moderate in those with no LPDFs (13.6 mm), (P < 0.1). Reduced luteal growth in ARA women with atypical versus typical and/or no LPDFs occurred simultaneously to greater luteal phase estradiol (199 vs 69.0, 78.4 ng/L) lower progesterone (7.38 vs 10.7, 13.8 ug/L), and lower inhibin A (36.3, 35.6, 51.2) (P < 0.1). The development of LPDFs as women age was associated with reduced luteal growth, greater estradiol, lower progesterone, and lower inhibin A. These findings provide preliminary evidence that variations in antral folliculogenesis contribute to luteal insufficiency during the menopausal transition.
Dharmarajan, A M; Bruce, N W; Meyer, G T
1989-01-01
Progesterone secretion by the corpora lutea (CL) of rats and rabbits declines from a peak, at about Day 16 of gestation, to near term (Day 22 rats and 28 rabbits). However there are major differences between the two species in CL growth and blood flow over this period. In the present work quantitative histological measurements were made of CL at these stages to examine the accompanying structural changes. Eight rats and five rabbits were examined at each stage: standard morphometric techniques were used. There was gross discrepancy between the two species in the histology of their CL at peak secretory activity. Although the proportions of the major tissue components were similar, the rabbit luteal cell (52 pl) was five times larger than that in the rat (9 pl). There was substantially less vascular and interstitial space in the rabbit, all characteristics which might affect transport processes between luteal cell cytoplasm and blood. Over the period examined, there was no change in CL volume in the rat but a 37% reduction in the rabbit due to loss of luteal cells. The vascular space in the rat, however, was reduced, whereas that in the rabbit declined only in proportion to the overall decrease in CL volume. These results show that structurally there is a substantial difference in patterns of early regression between the two species which reflect different mechanisms involved. Images Fig. 1 Fig. 2 PMID:2621138
The effect of metritis on luteal function in dairy cows
2013-01-01
Background Disturbed uterine involution impairs ovarian function in the first weeks after calving. This study analyzed the long-term effect of metritis on luteal function of 47 lactating Holstein-Friesian cows during the first four postpartum estrous cycles. Cows with abnormal uterine enlargement and malodorous lochia were classified as having metritis (group M, n = 18), and all others were considered healthy (group H, n = 29). Luteal size was measured once between days 9 and 13 of the first (group H, n = 11; group M, n = 12), second (group H, n = 23; group M, n = 18) and fourth (group H, n = 11; group M, n = 7) postpartum luteal phases. Serum progesterone concentration was measured at the same time. Sixteen cows (group H, n = 9; group M, n = 7) underwent transvaginal luteal biopsy for gene expression analysis of steroidogenic regulatory proteins during the second and fourth cycles. Cows with persistence of the corpus luteum (CL) underwent determination of luteal size, luteal biopsy and serum progesterone measurement once between days 29 and 33, followed by prostaglandin treatment to induce luteolysis. The same procedures were repeated once between days 9 and 13 of the induced cycle. Results The cows in group M had smaller first-cycle CLs than the cows in group H (p = 0.04), but progesterone concentrations did not differ between groups. Luteal size, progesterone concentration and gene expression did not differ between the two groups during the second and fourth cycles. Compared with healthy cows (10%), there was a trend (p = 0.07) toward a higher prevalence of persistent CLs in cows with metritis (33%). Persistent CLs were limited to the first cycle. Persistent CLs and the induced cyclic CLs did not differ with regard to the variables investigated. Conclusions An effect of metritis on luteal activity was apparent in the first postpartum estrous cycle. However, after the first postpartum cycle, no differences occurred
Astaxanthin increases progesterone production in cultured bovine luteal cells.
Kamada, Hachiro; Akagi, Satoshi; Watanabe, Shinya
2017-06-29
Although astaxanthin (AST) is known to be a strong antioxidant, its effects on reproductive function in domestic animals have not yet been elucidated in detail. Therefore, we investigated the effects of AST on luteal cells, which produce progesterone (P4), an important hormone for maintaining pregnancy. Luteal cells were prepared by collagenase dispersion of the corpus luteum (CL). The addition of racemic AST at a low concentration (production than RR-AST. When 1 mg/kg·body weight of SS-AST derived from green algae was fed to cows for 2 weeks, its concentration in blood plasma was 10.9 nM on average, which was sufficient to expect an in vitro effect on the production of P4 in cows. These results suggested the potential of SS-AST supplements for cows to elevate luteal function.
Park, Hyo-Jin; Park, Sun-Ji; Koo, Deog-Bon; Lee, Sang-Rae; Kong, Il-Keun; Ryoo, Jae-Woong; Park, Young-Il; Chang, Kyu-Tae; Lee, Dong-Seok
2014-09-15
We examined whether the three unfolded protein response (UPR) signaling pathways, which are activated in response to endoplasmic reticulum (ER)-stress, are involved in progesterone production in the luteal cells of the corpus luteum (CL) during the mouse estrous cycle. The luteal phase of C57BL/6 female mice (8 weeks old) was divided into two stages: the functional stage (16, 24, and 48 h) and the regression stage (72 and 96 h). Western blotting and reverse transcription (RT)-PCR were performed to analyze UPR protein/gene expression levels in each stage. We investigated whether ER stress affects the progesterone production by using Tm (0.5 μg/g BW) or TUDCA (0.5 μg/g BW) through intra-peritoneal injection. Our results indicate that expressions of Grp78/Bip, p-eIF2α/ATF4, p50ATF6, and p-IRE1/sXBP1 induced by UPR activation were predominantly maintained in functional and early regression stages of the CL. Furthermore, the expression of p-JNK, CHOP, and cleaved caspase3 as ER-stress mediated apoptotic factors increased during the regression stage. Cleaved caspase3 levels increased in the late-regression stage after p-JNK and CHOP expression in the early-regression stage. Additionally, although progesterone secretion and levels of steroidogenic enzymes decreased following intra-peritoneal injection of Tunicamycin, an ER stress inducer, the expression of Grp78/Bip, p50ATF6, and CHOP dramatically increased. These results suggest that the UPR signaling pathways activated in response to ER stress may play important roles in the regulation of the CL function. Furthermore, our findings enhance the understanding of the basic mechanisms affecting the CL life span. Copyright © 2014 Elsevier Inc. All rights reserved.
Transcriptomes of bovine ovarian follicular and luteal cells
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Sarah M. Romereim
2017-02-01
Full Text Available Affymetrix Bovine GeneChip® Gene 1.0 ST Array RNA expression analysis was performed on four somatic ovarian cell types: the granulosa cells (GCs and theca cells (TCs of the dominant follicle and the large luteal cells (LLCs and small luteal cells (SLCs of the corpus luteum. The normalized linear microarray data was deposited to the NCBI GEO repository (GSE83524. Subsequent ANOVA determined genes that were enriched (≥2 fold more or decreased (≤−2 fold less in one cell type compared to all three other cell types, and these analyzed and filtered datasets are presented as tables. Genes that were shared in enriched expression in both follicular cell types (GCs and TCs or in both luteal cells types (LLCs and SLCs are also reported in tables. The standard deviation of the analyzed array data in relation to the log of the expression values is shown as a figure. These data have been further analyzed and interpreted in the companion article “Gene expression profiling of ovarian follicular and luteal cells provides insight into cellular identities and functions” (Romereim et al., 2017 [1].
Energy Technology Data Exchange (ETDEWEB)
Zhong, Xia, E-mail: zhongxia1977@126.com [Department of Emergency, Provincial Hospital Affiliated to Shandong University, Jinan 250021 (China); Li, Xiaonan; Liu, Fuli; Tan, Hui [Department of Emergency, Provincial Hospital Affiliated to Shandong University, Jinan 250021 (China); Shang, Deya, E-mail: wenhuashenghuo1@163.com [Department of Emergency, Provincial Hospital Affiliated to Shandong University, Jinan 250021 (China)
2012-08-24
Highlights: Black-Right-Pointing-Pointer Omentin inhibited TNF-{alpha}-induced adhesion of THP-1 cells to HUVECs. Black-Right-Pointing-Pointer Omentin reduces expression of ICAM-1 and VCAM-1 induced by TNF-{alpha} in HUVECs. Black-Right-Pointing-Pointer Omentin inhibits TNF-{alpha}-induced ERK and NF-{kappa}B activation in HUVECs. Black-Right-Pointing-Pointer Omentin supreeses TNF-{alpha}-induced expression of ICAM-1 and VCAM-1 via ERK/NF-{kappa}B pathway. -- Abstract: In the present study, we investigated whether omentin affected the expression of intracellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1) in tumor necrosis factor-{alpha} (TNF-{alpha}) induced human umbilical vein endothelial cells (HUVECs). Our data showed that omentin decreased TNF-{alpha}-induced expression of ICAM-1 and VCAM-1 in HUVECs. In addition, omentin inhibited TNF-{alpha}-induced adhesion of THP-1 cells to HUVECs. Further, we found that omentin inhibited TNF-{alpha}-activated signal pathway of nuclear factor-{kappa}B (NF-{kappa}B) by preventing NF-{kappa}B inhibitory protein (I{kappa}B{alpha}) degradation and NF-{kappa}B/DNA binding activity. Omentin pretreatment significantly inhibited TNF-{alpha}-induced ERK activity and ERK phosphorylation in HUVECs. Pretreatment with PD98059 suppressed TNF-{alpha}-induced NF-{kappa}B activity. Omentin, NF-kB inhibitor (BAY11-7082) and ERK inhibitor (PD98059) reduced the up-regulation of ICAM-1 and VCAM-1 induced by TNF-{alpha}. These results suggest that omentin may inhibit TNF-{alpha}-induced expression of adhesion molecules in endothelial cells via blocking ERK/NF-{kappa}B pathway.
Degradation of high density lipoprotein in cultured rat luteal cells
International Nuclear Information System (INIS)
Rajan, V.P.; Menon, K.M.J.
1986-01-01
In rat ovary luteal cells, degradation of high density lipoprotein (HDL) to tricholoracetic acid (TCA)-soluble products accounts for only a fraction of the HDL-derived cholesterol used for steroidogenesis. In this study the authors have investigated the fate of 125 I]HDL bound to cultured luteal cells using pulse-chase technique. Luteal cell cultures were pulse labeled with [ 125 I]HDL 3 and reincubated in the absence of HDL. By 24 h about 50% of the initallay bound radioactivity was released into the medium, of which 60-65% could be precipitated with 10% TCA. Gel filtration of the chase incubation medium on 10% agarose showed that the amount of TCA-soluble radioactivity was nearly completely accounted for by a sharp peak in the low molecular weight region which was identified as 96% monoiodotyrosine by paper chromatography. The TCA-precipitable radioactivity was nearly completely accounted for by a sharp peak in the low molecular weight region which was identified as 96% monoiodotyrosine by paper chromatography. The TCA-precipitable radioactivity eluted over a wide range of molecular weights (15,000-80,000), and there was very little intact HDL present. Electrophoresis of the chase medium showed that component of the TCA-precipitable portion had mobility similar to apo AI. Lysosomal inhibitors of receptor-mediated endocytosis had no effect on the composition or quantity of radioactivity released during chase incubation. The results show that HDL 3 binding to luteal cells is followed by complete degradation of the lipoprotein, although the TCA-soluble part does not reflect the extent of degradation
The methoxychlor metabolite, HPTE, inhibits rat luteal cell progesterone production.
Akgul, Yucel; Derk, Raymond C; Meighan, Terence; Rao, K Murali Krishna; Murono, Eisuke P
2011-07-01
The methoxychlor metabolite, HPTE, was shown to inhibit P450-cholesterol side-chain cleavage (P450scc) activity resulting in decreased progesterone production by cultured ovarian follicular cells in previous studies. It is not known whether HPTE has any effect on progesterone formation by the corpus luteum. Exposure to 100 nM HPTE reduced progesterone production by luteal cells with progressive declines to progesterone formation and P450scc catalytic activity of hCG- or 8 Br-cAMP-stimulated luteal cells. However, HPTE did not alter mRNA and protein levels of P450scc. Compounds acting as estrogen (17 β-estradiol, bisphenol-A or octylphenol), antiestrogen (ICI) or antiandrogen (monobutyl phthalate, flutamide or M-2) added alone to luteal cells did not mimic the action of HPTE on progesterone and P450scc activity. These results suggest that HPTE directly inhibits P450scc catalytic activity resulting in reduced progesterone formation, and this action was not mediated through estrogen or androgen receptors. Published by Elsevier Inc.
Luteal blood flow in patients undergoing GnRH agonist long protocol
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Takasaki Akihisa
2011-01-01
Full Text Available Abstract Background Blood flow in the corpus luteum (CL is closely related to luteal function. It is unclear how luteal blood flow is regulated. Standardized ovarian-stimulation protocol with a gonadotropin-releasing hormone agonist (GnRHa long protocol causes luteal phase defect because it drastically suppresses serum LH levels. Examining luteal blood flow in the patient undergoing GnRHa long protocol may be useful to know whether luteal blood flow is regulated by LH. Methods Twenty-four infertile women undergoing GnRHa long protocol were divided into 3 groups dependent on luteal supports; 9 women were given ethinylestradiol plus norgestrel (Planovar orally throughout the luteal phase (control group; 8 women were given HCG 2,000 IU on days 2 and 4 day after ovulation induction in addition to Planovar (HCG group; 7 women were given vitamin E (600 mg/day orally throughout the luteal phase in addition to Planovar (vitamin E group. Blood flow impedance was measured in each CL during the mid-luteal phase by transvaginal color-pulsed-Doppler-ultrasonography and was expressed as a CL-resistance index (CL-RI. Results Serum LH levels were remarkably suppressed in all the groups. CL-RI in the control group was more than the cutoff value (0.51, and only 2 out of 9 women had CL-RI values Conclusion Patients undergoing GnRHa long protocol had high luteal blood flow impedance with very low serum LH levels. HCG administration improved luteal blood flow impedance. This suggests that luteal blood flow is regulated by LH.
Directory of Open Access Journals (Sweden)
Hoffmann Bernd
2011-08-01
Full Text Available Abstract Background Endocrine mechanisms governing canine reproductive function remain still obscure. Progesterone (P4 of luteal origin is required for maintenance of pregnancy. Corpora lutea (CL are gonadotrop-independent during the first third of dioestrus; afterwards prolactin (PRL is the primary luteotropic factor. Interestingly, the increasing PRL levels are accompanied by decreasing P4 concentrations, thus luteal regression/luteolysis occurs in spite of an increased availability of gonadotropic support. PRL acts through its receptor (PRLr, the expression of which has not yet been thoroughly investigated at the molecular and cellular level in the dog. Methods The expression of PRLr was assessed in CL of non-pregnant dogs during the course of dioestrus (days 5, 15, 25, 35, 45, 65 post ovulation; p.o. as well as in CL, the utero/placental compartments (Ut/Pl and interplacental free polar zones (interplacental sites from pregnant dogs during the pre-implantation, post-implantation and mid-gestation period of pregnancy and during the normal and antigestagen-induced luteolysis. Expression of PRLr was tested by Real Time PCR, immunohistochemistry and in situ hybridization. Results In non-pregnant CL the PRLr expression was significantly upregulated at day 15 p.o. and decreased significantly afterwards, towards the end of dioestrus. CL of pregnancy showed elevated PRLr expression until mid gestation while prepartal downregulation was observed. Interestingly, placental but not interplacental expression of PRLr was strongly time-related; a significant upregulation was observed towards mid-gestation. Within the CL PRLr was localized to the luteal cells; in the Ut/Pl it was localized to the fetal trophoblast and epithelial cells of glandular chambers. Moreover, in mid-pregnant animals treated with an antigestagen, both the luteal and placental, but not the uterine PRLr were significantly downregulated. Conclusions The data presented suggest that the
Kowalewski, Mariusz P; Michel, Erika; Gram, Aykut; Boos, Alois; Guscetti, Franco; Hoffmann, Bernd; Aslan, Selim; Reichler, Iris
2011-08-03
Endocrine mechanisms governing canine reproductive function remain still obscure. Progesterone (P4) of luteal origin is required for maintenance of pregnancy. Corpora lutea (CL) are gonadotrop-independent during the first third of dioestrus; afterwards prolactin (PRL) is the primary luteotropic factor. Interestingly, the increasing PRL levels are accompanied by decreasing P4 concentrations, thus luteal regression/luteolysis occurs in spite of an increased availability of gonadotropic support. PRL acts through its receptor (PRLr), the expression of which has not yet been thoroughly investigated at the molecular and cellular level in the dog. The expression of PRLr was assessed in CL of non-pregnant dogs during the course of dioestrus (days 5, 15, 25, 35, 45, 65 post ovulation; p.o.) as well as in CL, the utero/placental compartments (Ut/Pl) and interplacental free polar zones (interplacental sites) from pregnant dogs during the pre-implantation, post-implantation and mid-gestation period of pregnancy and during the normal and antigestagen-induced luteolysis. Expression of PRLr was tested by Real Time PCR, immunohistochemistry and in situ hybridization. In non-pregnant CL the PRLr expression was significantly upregulated at day 15 p.o. and decreased significantly afterwards, towards the end of dioestrus. CL of pregnancy showed elevated PRLr expression until mid gestation while prepartal downregulation was observed. Interestingly, placental but not interplacental expression of PRLr was strongly time-related; a significant upregulation was observed towards mid-gestation. Within the CL PRLr was localized to the luteal cells; in the Ut/Pl it was localized to the fetal trophoblast and epithelial cells of glandular chambers. Moreover, in mid-pregnant animals treated with an antigestagen, both the luteal and placental, but not the uterine PRLr were significantly downregulated. The data presented suggest that the luteal provision of P4 in both pregnant and non-pregnant dogs
Arikan, Ş; Kalender, H; Simsek, O
2010-12-01
The aim of the present study was to evaluate the effects of cholesterol on progesterone production during long-term culturing of luteal cell subpopulations at early and late luteal stages of the goat corpora lutea. Corpora lutea were collected from Angora goats on days 5 and 15 of the oestrous cycle. Luteal cells were isolated by collagenase digestion. The cells were separated into two distinct subpopulations by Percoll density-gradient centrifugation. Both subpopulations of luteal cells staining positively for 3β-HSD activities (5 × 10(4) cell/well) were cultured with or without 22(R)-hydroxycholesterol (22R-HC) in serum-free culture medium for periods of up to 7 days. Cells were incubated with serum (10%) for the first 18 h of incubation followed by serum-free medium. Cell treatment (10 and 20 μg/ml) was performed on days 1, 3 and 5. Treatment of cells with both concentrations of 22R-HC resulted in significant (p 0.05) on progesterone production in both fractions of cells throughout 7 days of incubation. Treatment of the cells with cholesterol resulted in 2.5- and 9.0-fold increases in progesterone accumulation on day 3 of incubation. Steroid production was maintained throughout the incubations when cells are incubated in serum-free media treated with cholesterol and ITS premix. Cells collected from higher density of percoll layers produced 2.82 and 2.32 times more progesterone, in comparison to the lover density percoll layer, on days 5 and 15 of the oestrous cycle in untreated cell groups, respectively. Progesterone accumulation was decreased as incubation time advanced in all groups of untreated cells. These results demonstrated that goat luteal cell subpopulations secrete substantial amounts of progesterone in response to cholesterol treatment at least for 7 days, and cholesterol is required as progesterone precursor for maintaining a high-level steroidogenesis during long-life culturing of both cell subpopulations. © 2010 Blackwell
Uniyal, S; Panda, R P; Chouhan, V S; Yadav, V P; Hyder, I; Dangi, S S; Gupta, M; Khan, F A; Sharma, G T; Bag, S; Sarkar, M
2015-01-01
This study investigated the expression and localization of insulin-like growth factor (IGF) system at different stages of buffalo CL and the role of IGF-I in stimulating vascular endothelial growth factor (VEGF) and progesterone (P4) production in cultured luteal cells. The mRNA expression of IGF system, VEGF, steroidogenic acute regulatory protein, P450scc, and hydroxysteroid dehydrogenase (HSD) was investigated by quantitative real-time polymerase chain reaction (PCR). Protein expression of IGF was demonstrated by Western blot and localization by immunohistochemistry. Progesterone and VEGF production was assayed using RIA and ELISA. A relatively high mRNA expression of IGF-I and IGF-II in early, mid- and late luteal phases with immunoreactivity mostly restricted to cytoplasm of large luteal cells indicates their autocrine role, whereas very weak immunoreactivity in endothelial cells during the mid-luteal phase indicates their paracrine role. Insulin-like growth factor receptors, IGF-IR and IGF-IIR, were restricted to large luteal cells with high mRNA and protein expressions in the mid-luteal phase. The significantly higher expression of insulin-like growth factor binding protein (IGFBP)-1, -3, -5, and -6 in the early or mid-luteal phase suggested their stimulatory role, whereas that of IGFBP-2 and -4 in mid-, late, and regressive luteal stages implied their inhibitory role. The mRNA expressions of key steroidogenic factors and VEGF were significantly higher (P production (P production of VEGF in luteal cells and steroid synthesis through the production of key steroidogenic factors. Copyright © 2015 Elsevier Inc. All rights reserved.
The role of adrenergic activation on murine luteal cell viability and progesterone production.
Wang, Jing; Tang, Min; Jiang, Huaide; Wu, Bing; Cai, Wei; Hu, Chuan; Bao, Riqiang; Dong, Qiming; Xiao, Li; Li, Gang; Zhang, Chunping
2016-09-15
Sympathetic innervations exist in mammalian CL. The action of catecholaminergic system on luteal cells has been the focus of a variety of studies. Norepinephrine (NE) increased progesterone secretion of cattle luteal cells by activating β-adrenoceptors. In this study, murine luteal cells were treated with NE and isoprenaline (ISO). We found that NE increased the viability of murine luteal cells and ISO decreased the viability of luteal cells. Both NE and ISO promoted the progesterone production. Nonselective β-adrenergic antagonist, propranolol reversed the effect of ISO on cell viability but did not reverse the effect of NE on cell viability. Propranolol blocked the influence of NE and ISO on progesterone production. These results reveal that the increase of luteal cell viability induced by NE is not dependent on β-adrenergic activation. α-Adrenergic activation possibly contributes to it. Both NE and ISO increased progesterone production through activating β-adrenergic receptor. Further study showed that CyclinD2 is involved in the increase of luteal cell induced by NE. 3β-Hydroxysteroid dehydrogenase, LHR, steroidogenic acute regulatory protein (StAR), and PGF2α contribute to the progesterone production induced by NE and ISO. Copyright © 2016 Elsevier Inc. All rights reserved.
Zatta, Sophie; Rehrauer, Hubert; Gram, Aykut; Boos, Alois; Kowalewski, Mariusz Pawel
2017-09-27
In the domestic dog, corpora lutea (CL) are the only source of progesterone (P4), both in pregnant and non-pregnant cycles because there is no placental steroidogenesis. The absence of an endogenous luteolysin in absence of pregnancy results in long-lasting physiological pseudopregnancy, strongly contrasting with the acute luteolysis observed prepartum. The underlying biological mechanisms and the involvement of P4 signalling remain, however, not fully understood. Therefore, here, next-generation sequencing (RNA-Seq) was performed on CL from the late luteal phase and compared with normally luteolyzing CL collected at the prepartum P4 decrease. The contrast "luteal regression over luteolysis" yielded 1595 differentially expressed genes (DEG). The CL in late luteal regression were predominantly associated with functional terms linked to extracellular matrix (p = 5.52e-05). Other terms related to transcriptional activity (p = 2.45e-04), and steroid hormone signalling (p = 2.29e-04), which were more highly represented in late regression than during luteolysis. The prepartum luteolysis was associated with immune inflammatory responses (p = 2.87e-14), including acute-phase reaction (p = 4.10e-06). Immune system-related events were also more highly represented in CL derived from normal luteolysis (p = 7.02e-04), compared with those from dogs in which luteolysis was induced with an antigestagen (1480 DEG in total). Additionally, the withdrawal of P4 at mid-gestation resulted in 92 DEG; over-represented terms enriched in antigestagen-treated dogs were related to the inflammatory response (p = 0.005) or response to IL1 (p = 7.29e-05). Terms related to proliferation, e.g., centrosome organization (p = 0.002) and steroid metabolic processes (p = 0.001), prevailed at mid-gestation. Thereby, our results revealed the nature of luteotropic effects of P4 within canine CL. It appears that, even though they result in diminished steroidogenic output, the effect of
Directory of Open Access Journals (Sweden)
Meenakumari K.J.
2004-01-01
Full Text Available The causes of luteal phase progesterone deficiency in polycystic ovary syndrome (PCOS are not known. To determine the possible involvement of hyperinsulinemia in luteal phase progesterone deficiency in women with PCOS, we examined the relationship between progesterone, luteinizing hormone (LH and insulin during the luteal phase and studied the effect of metformin on luteal progesterone levels in PCOS. Patients with PCOS (19 women aged 18-35 years were treated with metformin (500 mg three times daily for 4 weeks prior to the test cycle and throughout the study period, and submitted to ovulation induction with clomiphene citrate. Blood samples were collected from control (N = 5, same age range as PCOS women and PCOS women during the late follicular (one sample and luteal (3 samples phases and LH, insulin and progesterone concentrations were determined. Results were analyzed by one-way analysis of variance (ANOVA, Duncan's test and Karl Pearson's coefficient of correlation (r. The endocrine study showed low progesterone level (4.9 ng/ml during luteal phase in the PCOS women as compared with control (21.6 ng/ml. A significant negative correlation was observed between insulin and progesterone (r = -0.60; P < 0.01 and between progesterone and LH (r = -0.56; P < 0.05 concentrations, and a positive correlation (r = 0.83; P < 0.001 was observed between LH and insulin. The study further demonstrated a significant enhancement in luteal progesterone concentration (16.97 ng/ml in PCOS women treated with metformin. The results suggest that hyperinsulinemia/insulin resistance may be responsible for low progesterone levels during the luteal phase in PCOS. The luteal progesterone level may be enhanced in PCOS by decreasing insulin secretion with metformin.
Characterization of hormonal profiles during the luteal phase in regularly menstruating women.
Ecochard, Rene; Bouchard, Thomas; Leiva, Rene; Abdulla, Saman; Dupuis, Olivier; Duterque, Olivia; Garmier Billard, Marie; Boehringer, Hans; Genolini, Christophe
2017-07-01
To characterize the variability of hormonal profiles during the luteal phase in normal cycles. Observational study. Not applicable. Ninety-nine women contributing 266 menstrual cycles. The women collected first morning urine samples that were analyzed for estrone-3-glucuronide, pregnanediol-3-alpha-glucuronide (PDG), FSH, and LH. The women had serum P tests (twice per cycle) and underwent ultrasonography to identify the day of ovulation. The luteal phase was divided into three parts: the early luteal phase with increasing PDG (luteinization), the midluteal phase with PDG ≥10 μg/mg Cr (progestation), and the late luteal phase (luteolysis) when PDG fell below 10 μg/mg Cr. Long luteal phases begin with long luteinization processes. The early luteal phase is marked by low PDG and high LH levels. Long luteinization phases were correlated with low E1G and low PDG levels at day 3. The length of the early luteal phase is highly variable between cycles of the same woman. The duration and hormonal levels during the rest of the luteal phase were less correlated with other characteristics of the cycle. The study showed the presence of a prolonged pituitary activity during the luteinization process, which seems to be modulated by an interaction between P and LH. This supports a luteal phase model with three distinct processes: the first is a modulated luteinization process, whereas the second and the third are relatively less modulated processes of progestation and luteolysis. Copyright © 2017 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.
The luteal phase after GnRH-agonist triggering of ovulation: present and future perspectives
DEFF Research Database (Denmark)
Humaidan, Peter; Papanikolaou, E G; Kyrou, D
2012-01-01
In stimulated IVF/intracytoplasmic sperm injection cycles, the luteal phase is disrupted, necessitating luteal-phase supplementation. The most plausible reason behind this is the ovarian multifollicular development obtained after ovarian stimulation, resulting in supraphysiological steroid...... with a GnRH agonist instead of human chorionic gonadotrophin (HCG). The first studies applying this concept, however, showed a very poor pregnancy rate, despite standard luteal-phase support with progesterone. This review discusses the reason for the poor results and the newest studies, using GnRH agonist...
Spady, Richard; Stouli, Sami
2012-01-01
We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...
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Lainas George T
2012-08-01
Full Text Available Abstract Background Management of established severe OHSS requires prolonged hospitalization, occasionally in intensive care units, accompanied by multiple ascites punctures, correction of intravascular fluid volume and electrolyte imbalance. The aim of the present study was to evaluate whether it is feasible to manage women with severe OHSS as outpatients by treating them with GnRH antagonists in the luteal phase. Methods This is a single-centre, prospective, observational, cohort study. Forty patients diagnosed with severe OHSS, five days post oocyte retrieval, were managed as outpatients after administration of GnRH antagonist (0.25 mg daily from days 5 to 8 post oocyte retrieval, combined with cryopreservation of all embryos. The primary outcome measure was the proportion of patients with severe OHSS, in whom outpatient management was not feasible. Results 11.3% (95% CI 8.3%-15.0% of patients (40/353 developed severe early OHSS. None of the 40 patients required hospitalization following luteal antagonist administration and embryo cryopreservation. Ovarian volume, ascites, hematocrit, WBC, serum oestradiol and progesterone decreased significantly (P Conclusions The current study suggests, for the first time, that successful outpatient management of severe OHSS with antagonist treatment in the luteal phase is feasible and is associated with rapid regression of the syndrome, challenging the dogma of inpatient management. The proposed management is a flexible approach that minimizes unnecessary embryo transfer cancellations in the majority (88.7% of high risk for OHSS patients.
Investigation of activation cross-sections of alpha-induced nuclear reactions on natural cadmium
Energy Technology Data Exchange (ETDEWEB)
Khandaker, Mayeen Uddin [Department of Physics, Kyungpook National University, Daegu 702-701 (Korea, Republic of); Department of Physics, University of Malaya, 50603 Kuala Lumpur (Malaysia); Kim, Kwangsoo [Department of Physics, Kyungpook National University, Daegu 702-701 (Korea, Republic of); Lee, Manwoo [Department of Physics, Kyungpook National University, Daegu 702-701 (Korea, Republic of); Research Center, Dongnam Institute of Radiological and Medical Science, Busan 619-953 (Korea, Republic of); Kim, Guinyun, E-mail: gnkim@knu.ac.kr [Department of Physics, Kyungpook National University, Daegu 702-701 (Korea, Republic of)
2014-08-15
We measured production cross-sections of Sn, In, and Cd radionuclides from alpha-induced reactions on {sup nat}Cd from their respective threshold to 45 MeV by using a stacked-foil activation technique at the MC-50 cyclotron of the Korea Institute of Radiological and Medical Sciences. The results were compared with the earlier measurements as well as with the theoretical values obtained from the TENDL-2012 library based on the TALYS 1.4 code. Our measurements for the {sup 110,113g,117m}Sn, {sup 108m,108g,109g,110m,110g,111g,113m,114m,115m,116m,117m,117g}In, and {sup 111m,115g}Cd radionuclides in the energy region from the threshold energy to 45 MeV are in general good agreement with the other experimental data and calculated results. The integral yields for thick target were also deduced using the measured cross-sections and the stopping power of natural cadmium target and found in agreement with the directly measured yields available in the literature. The measured cross-sections find importance in various practical applications including nuclear medicine and improvement of nuclear model calculations.
Deficiencies in luteal function during re-initiation of cyclic breeding ...
African Journals Online (AJOL)
such conversion to large luteal cells occurs only during the early part of the oestrous cycle. There may even be stem .... anoestrous ewes, stimulation of the pre-ovulatory follicle ...... time relationships concerning oestrus, ovulation and electrical.
Luteal function during the estrous cycle in arginine-treated ewes fed different planes of nutrition.
Bass, Casie S; Redmer, Dale A; Kaminski, Samantha L; Grazul-Bilska, Anna T
2017-03-01
Functions of corpus luteum (CL) are influenced by numerous factors including hormones, growth and angiogenic factors, nutritional plane and dietary supplements such as arginine (Arg), a semi-essential amino acid and precursor for proteins, polyamines and nitric oxide (NO). The aim of this study was to determine if Arg supplementation to ewes fed different planes of nutrition influences: (1) progesterone (P4) concentrations in serum and luteal tissue, (2) luteal vascularity, cell proliferation, endothelial NO synthase (eNOS) and receptor (R) soluble guanylate cyclase β protein and mRNA expression and (3) luteal mRNA expression for selected angiogenic factors during the estrous cycle. Ewes (n = 111) were categorized by weight and randomly assigned to one of three nutritional planes: maintenance control (C), overfed (2× C) and underfed (0.6× C) beginning 60 days prior to onset of estrus. After estrus synchronization, ewes from each nutritional plane were assigned randomly to one of two treatments: Arg or saline. Serum and CL were collected at the early, mid and late luteal phases. The results demonstrated that: (1) nutritional plane affected ovulation rates, luteal vascularity, cell proliferation and NOS3, GUCY1B3, vascular endothelial growth factor (VEGF) and VEGFR2 mRNA expression, (2) Arg affected luteal vascularity, cell proliferation and NOS3, GUCY1B3, VEGF and VEGFR2 mRNA expression and (3) luteal vascularity, cell proliferation and the VEGF and NO systems depend on the stage of the estrous cycle. These data indicate that plane of nutrition and/or Arg supplementation can alter vascularization and expression of selected angiogenic factors in luteal tissue during the estrous cycle in sheep. © 2017 Society for Reproduction and Fertility.
Gupta, M; Dangi, S S; Chouhan, V S; Hyder, I; Babitha, V; Yadav, V P; Khan, F A; Sonwane, A; Singh, G; Das, G K; Mitra, A; Bag, S; Sarkar, M
2014-07-01
Evidence obtained during recent years provided has insight into the regulation of corpus luteum (CL) development, function, and regression by locally produced ghrelin. The present study was carried out to evaluate the expression and localization of ghrelin and its receptor (GHS-R1a) in bubaline CL during different stages of the estrous cycle and investigate the role of ghrelin on progesterone (P4) production along with messenger RNA (mRNA) expression of P4 synthesis intermediates. The mRNA and protein expression of ghrelin and GHS-R1a was significantly greater in mid- and late luteal phases. Both factors were localized in luteal cells, exclusively in the cytoplasm. Immunoreactivity of ghrelin and GHS-R1a was greater during mid- and late luteal phases. Luteal cells were cultured in vitro and treated with ghrelin each at 1, 10, and 100 ng/mL concentrations for 48 h after obtaining 75% to 80% confluence. At a dose of 1 ng/mL, there was no significant difference in P4 secretion between control and treatment group. At 10 and 100 ng/mL, there was a decrease (P production in buffalo. Copyright © 2014 Elsevier Inc. All rights reserved.
Luan, Y Y; Yao, Y M; Sheng, Z Y
2013-01-01
Within the immune system homeostasis is maintained by a myriad of mechanisms that include the regulation of immune cell activation and programmed cell death. The breakdown of immune homeostasis may lead to fatal inflammatory diseases. We set out to identify genes of tumor necrosis factor-alpha-induced protein 8 (TNFAIP8) family that has a functional role in the process of immune homeostasis. Tumor necrosis factor-alpha-induced protein 8 (TNFAIP8), which functions as an oncogenic molecule, is also associated with enhanced cell survival and inhibition of apoptosis. Tumor necrosis factor-alpha-induced protein 8-like 2 (TIPE2) governs immune homeostasis in both the innate and adaptive immune system and prevents hyper-responsiveness by negatively regulating signaling via T cell receptors and Toll-like receptors (TLRs). There also exist two highly homologous but uncharacterized proteins, TIPE1 and TIPE3. This review is an attempt to provide a summary of TNFAIP8 family associated with immune homeostasis and inflammatory cancer diseases.
Luteal activity of pregnant rats with hypo-and hyperthyroidism.
Silva, Juneo Freitas; Ocarino, Natália Melo; Serakides, Rogéria
2014-07-12
Luteal activity is dependent on the interaction of various growth factors, cytokines and hormones, including the thyroid hormones, being that hypo- and hyperthyroidism alter the gestational period and are also a cause of miscarriage and stillbirth. Because of that, we evaluated the proliferation, apoptosis and expression of angiogenic factors and COX-2 in the corpus luteum of hypo- and hyperthyroid pregnant rats. Seventy-two adult female rats were equally distributed into three groups: hypothyroid, hyperthyroid and control. Hypo- and hyperthyroidism were induced by the daily administration of propylthiouracil and L-thyroxine, respectively. The administration began five days before becoming pregnant and the animals were sacrificed at days 10, 14, and 19 of gestation. We performed an immunohistochemical analysis to evaluate the expression of CDC-47, VEGF, Flk-1 (VEGF receptor) and COX-2. Apoptosis was evaluated by the TUNEL assay. We assessed the gene expression of VEGF, Flk-1, caspase 3, COX-2 and PGF2α receptor using real time RT-PCR. The data were analyzed by SNK test. Hypothyroidism reduced COX-2 expression on day 10 and 19 (P Hyperthyroidism increased the expression of COX-2 on day 19 (P hyperthyroid animals, being this effect dependent of the gestational period.
The Luteal Phase after GnRHa Trigger-Understanding An Enigma
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Kathrine Leth-Moller
2014-11-01
Full Text Available The luteal phase of all stimulated in vitro fertilization/intra-cytoplasmic sperm injection (IVF/ICSI cycles is disrupted, which makes luteal phase support (LPS mandatory. The cause of the disruption is thought to be the multifollicular development achieved during ovarian stimulation which results in supraphysiological concentrations of steroids secreted by a high number of corpora lutea during the early luteal phase. This will directly inhibit luteinizing hormone (LH secretion by the pituitary via negative feedback at the level of the hypothalamic-pituitary axis, leading to a luteal phase defect. With the introduction of the gonadotropin-releasing hormone (GnRH antagonist protocol, it became feasible to trigger final oocyte maturation and ovulation with a single bolus of GnRH agonist (GnRHa as an alternative to human chorionic gonadotropin (hCG. GnRHa triggering presents several advantages, including the reduction in or even elimination of ovarian hyperstimulation syndrome. Despite the potential advantages of GnRHa triggering, previous randomized controlled trials reported a poor clinical outcome with high rates of early pregnancy losses, despite supplementation with a standard LPS in the form of progesterone and estradiol. Following these disappointing results, several studies now report a luteal phase rescue after modifications of the LPS, resulting in a reproductive outcome comparable to that seen after hCG triggering. We herein review luteal phase differences between the natural cycle, hCG trigger and GnRHa trigger and present the most recent data on handling the luteal phase after GnRHa triggering.
The Adequate Corpus Luteum: miR-96 Promotes Luteal Cell Survival and Progesterone Production.
Mohammed, Bushra T; Sontakke, Sadanand D; Ioannidis, Jason; Duncan, W Colin; Donadeu, F Xavier
2017-07-01
Inadequate progesterone production from the corpus luteum is associated with pregnancy loss. Data available in model species suggest important roles of microRNAs (miRNAs) in luteal development and maintenance. To comprehensively investigate the involvement of miRNAs during the ovarian follicle-luteal transition. The effects of specific miRNAs on survival and steroid production by human luteinized granulosa cells (hLGCs) were tested using specific miRNA inhibitors. Candidate miRNAs were identified through microarray analyses of follicular and luteal tissues in a bovine model. An academic institution in the United Kingdom associated with a teaching hospital. hLGCs were obtained by standard transvaginal follicular-fluid aspiration from 35 women undergoing assisted conception. Inhibition of candidate miRNAs in vitro. Levels of miRNAs, mRNAs, FOXO1 protein, apoptosis, and steroids were measured in tissues and/or cultured cells. Two specific miRNA clusters, miR-183-96-182 and miR-212-132, were dramatically increased in luteal relative to follicular tissues. miR-96 and miR-132 were the most upregulated miRNAs within each cluster. Database analyses identified FOXO1 as a putative target of both these miRNAs. In cultured hLGCs, inhibition of miR-96 increased apoptosis and FOXO1 protein levels, and decreased progesterone production. These effects were prevented by small interfering RNA-mediated downregulation of FOXO1. In bovine luteal cells, miR-96 inhibition also led to increases in apoptosis and FOXO1 protein levels. miR-96 targets FOXO1 to regulate luteal development through effects on cell survival and steroid production. The miR-183-96-182 cluster could provide a novel target for the manipulation of luteal function. Copyright © 2017 Endocrine Society
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Brodzki Piotr
2014-03-01
Full Text Available The experiment was conducted on 30 Holstein-Friesian cows: 10 cows in the follicular phase of the cycle and in the luteal phase 10 d later, 10 cows with follicular cysts, and 10 with luteal cysts. The presence of the ovarian structures was confirmed by ultrasonography. Serum levels of progesterone and 17β-oestradiol were tested with ELISA. Samples for cytological examination were collected from the uterus of all cows using a cytological brush. Following staining, the smears were evaluated in terms of quality and percentages of endometrial cells. In the follicular phase of the oestrous cycle, cells of type A - superficial cells (64.6 ± 4.48 were proportionally the largest group of cells. Cells of type C - basal cells (19.8 ± 2.75 were also present. In the luteal phase, the highest percentage of cells was of type B - intermediate cells (76.9 ± 4.26. When follicular cysts were present on the ovaries, the cytology resembled the follicular phase of the cycle, but with many younger type C cells (33.1 ± 4.11. In the case of luteal cysts on the ovaries, the cytology was similar to that of the luteal phase of the cycle, however with a lower percentage of type B cells (58.1 ± 5.71, and a slightly higher percentage of the other types. The differences in the cytological image of the uterus when different ovarian structures are present, depend on the hormonal activity of those structures. Due to the lack of literature data, the results of the study are important as a model, and may substantially facilitate identification of phases of the oestrus cycle, or the pathologies described, as well as indicate the current status of the endometrium
Zhang, Hongyang; Welch, William J.; Zamar, Ruben H.
2017-01-01
Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a "Regression Phalanx" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensi...
Colour Doppler Ultrasonography as a Tool to Assess Luteal Function in Santa Inês Ewes.
Figueira, L M; Fonseca, J F; Arashiro, Ekn; Souza-Fabjan, Jmg; Ribeiro, Acs; Oba, E; Viana, Jhm; Brandão, F Z
2015-08-01
The aim of this study was to evaluate luteal dynamics in the Santa Inês ewes using colour Doppler (CD) ultrasonography. Oestrus was synchronized in nulliparous females (n = 18), and subsequently, they were only teased (n = 6) or teased and mated (n = 12). Blood samples were collected daily for plasma progesterone (P4 ) concentrations. Ultrasonographic images of corpora lutea (CL) in CD mode were obtained for further analysis in its largest diameter. The CD mode allowed an early sequential monitoring of CL that was visualized by the first time 0.77 ± 0.62 days after ovulation, with luteal area 29.68 ± 13.21 mm(2) . During the luteogenesis, a progressive increase was observed, followed by a plateau of luteal area, vascularization area and plasma concentrations of P4 reaching maximum values in D11 (124.0 ± 38.0 mm(2) , 52.78 ± 24.08 mm(2) and 11.23 ± 4.89 ng/ml, respectively). In the luteolysis, the plasma concentrations of P4 decreased sharply, whereas luteal and vascularization area gradually. The vascularization area was positively correlated with plasma concentrations of P4 during the luteogenesis (r = 0.22) and luteolysis (r = 0.48). The luteal dynamics of Santa Inês ewes showed patterns similar to those observed in other sheep breeds studied. The CD ultrasonography has the potential to be used as a tool to assess luteal function in sheep. © 2015 Blackwell Verlag GmbH.
Luteal phase of the menstrual cycle increases sweating rate during exercise
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Garcia A.M.C.
2006-01-01
Full Text Available The present study evaluated whether the luteal phase elevation of body temperature would be offset during exercise by increased sweating, when women are normally hydrated. Eleven women performed 60 min of cycling exercise at 60% of their maximal work load at 32ºC and 80% relative air humidity. Each subject participated in two identical experimental sessions: one during the follicular phase (between days 5 and 8 and the other during the luteal phase (between days 22 and 25. Women with serum progesterone >3 ng/mL, in the luteal phase were classified as group 1 (N = 4, whereas the others were classified as group 2 (N = 7. Post-exercise urine volume (213 ± 80 vs 309 ± 113 mL and specific urine gravity (1.008 ± 0.003 vs 1.006 ± 0.002 changed (P < 0.05 during the luteal phase compared to the follicular phase in group 1. No menstrual cycle dependence was observed for these parameters in group 2. Sweat rate was higher (P < 0.05 in the luteal (3.10 ± 0.81 g m-2 min-1 than in the follicular phase (2.80 ± 0.64 g m-2 min-1 only in group 1. During exercise, no differences related to menstrual cycle phases were seen in rectal temperature, heart rate, rate of perceived exertion, mean skin temperature, and pre- and post-exercise body weight. Women exercising in a warm and humid environment with water intake seem to be able to adapt to the luteal phase increase of basal body temperature through reduced urinary volume and increased sweating rate.
The Luteal Phase after GnRHa Trigger-Understanding An Enigma
DEFF Research Database (Denmark)
Leth-Moller, Kathrine; Hammer Jagd, Sandra; Humaidan, Peter
2014-01-01
results in supraphysiological concentrations of steroids se- creted by a high number of corpora lutea during the early luteal phase. This will directly inhibit luteinizing hormone (LH) secretion by the pituitary via negative feedback at the level of the hypothalamic-pituitary axis, leading to a luteal...... several advantages, including the reduction in or even elimination of ovarian hyperstimulation syndrome. Despite the potential advantages of GnRHa trig- gering, previous randomized controlled trials reported a poor clinical outcome with high rates of early pregnancy losses, despite supplementation...
Luteal phase administration of agents for the treatment of premenstrual dysphoric disorder.
Freeman, Ellen W
2004-01-01
This review focuses on current information about luteal phase administration (i.e. typically for the last 2 weeks of the menstrual cycle) of pharmacological agents for the treatment of premenstrual dysphoric disorder (PMDD). Compared with continuous administration, a luteal phase administration regimen reduces the exposure to medication and lowers the costs of treatment. Based on evidence from randomised clinical trials, SSRIs are the first-line treatment for PMDD at this time. Of these agents, sertraline, fluoxetine and paroxetine (as an extended-release formulation) are approved by the US FDA for luteal phase, as well as continuous, administration. Clinical trials of these agents and citalopram have demonstrated that symptom reduction is similar with both administration regimens. When used to treat PMDD, SSRI doses are consistent with those used for major depressive disorder. The medications are well tolerated; discontinuation symptoms with this intermittent administration regimen have not been reported. Other medications that have been examined in clinical trials for PMDD or severe premenstrual syndrome (PMS) using luteal phase administration include buspirone, alprazolam, tryptophan and progesterone. Buspirone and alprazolam show only modest efficacy in PMS (in some but not all studies), but there may be a lower incidence of sexual adverse effects with these medications than with SSRIs. Symptom reduction with tryptophan was significantly greater than with placebo, but the availability of this medication is strictly limited because of safety concerns. Progesterone has consistently failed to show efficacy for severe PMS/PMDD in large, randomised, placebo-controlled trials.
Treatment of premenstrual dysphoric disorder with luteal phase dosing of sertraline.
Halbreich, Uriel; Kahn, Linda S
2003-11-01
Sertraline (Zoloft, Pfizer Inc.) is a selective serotonin re-uptake inhibitor (SSRI) which has been approved by the US FDA for the treatment of premenstrual dysphoric disorder (PMDD). PMDD is a severe form of premenstrual syndrome (PMS) which affects at least 5 - 8% of women of reproductive age. It is characterised by cyclic appearance at the late luteal phase of the menstrual cycle, and disappearance following the beginning of menses, with no symptoms during at least 1 week of the cycle - usually during the mid-follicular phase. Due to the cyclic luteal occurrence of PMDD, luteal phase dosing of SSRIs has been suggested and proven effective for sertraline as well as several other SSRIs. The clinical response of sertraline is reported to be within several days following initiation of treatment. Despite repeated cyclic discontinuation, no significant discontinuation adverse effects have been reported. In addition to its proven clinical efficacy, luteal-phase dosing may offer the advantages of minimising adverse effects of SSRIs while reducing the personal and economic burden of taking a prescription medication continuously for long periods and thus increasing compliance.
Destro, F C; Martin, I; Landim-Alvarenga, Fdc; Ferreira, Jcp; Pate, J L
2016-10-01
The aim of this study was to evaluate the effects of concanavalin A (CONA) on the progesterone (P4) production by bovine steroidogenic luteal cells (LCs) in vitro. Luteal cells were collected during the mid-luteal stage (at 10-12 days following ovulation) and processed in the laboratory. Luteal cells were grown for 7 days in a humid atmosphere with 5% CO2 , with or without 10% foetal bovine serum, and were subjected to the following treatments: control: no treatment; CONA (10 μg/ml); LH (100 μg/ml); CONA + LH; LH (100 μg/ml) + prostaglandin F2α (PGF2α) (10 ng/ml); CONA + LH + PGF2α. Samples of the culture media were collected on days 1 (D1) and 7 (D7) for P4 quantification. The cells were counted on D7 of culture. Differences between treatments were considered statistically significant at p < .05. Culture in the presence of CONA decreased the P4-secreting capacity of LCs on D7 of culture, particularly in the absence of serum. The cell numbers did not change between treatments. © 2016 Blackwell Verlag GmbH.
OCT-4 expression in follicular and luteal phase endometrium: a pilot study
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Huber Johannes C
2010-04-01
Full Text Available Abstract Background The stem cell marker Octamer-4 (OCT-4 is expressed in human endometrium. Menstrual cycle-dependency of OCT-4 expression has not been investigated to date. Methods In a prospective, single center cohort study of 98 women undergoing hysteroscopy during the follicular (n = 49 and the luteal (n = 40 phases of the menstrual cycle, we obtained endometrial samples. Specimens were investigated for OCT-4 expression on the mRNA and protein levels using reverse transcriptase polymerase chain reaction (RT-PCR and immunohistochemistry. Expression of OCT-4 was correlated to menstrual cycle phase. Results Of 89 women sampled, 49 were in the follicular phase and 40 were in the luteal phase. OCT-4 mRNA was detected in all samples. Increased OCT-4 mRNA levels in the follicular and luteal phases was found in 35/49 (71% and 27/40 (68% of women, respectively (p = 0.9. Increased expression of OCT-4 protein was identified in 56/89 (63% samples. Increased expression of OCT-4 protein in the follicular and luteal phases was found in 33/49 (67% and 23/40 (58% of women, respectively (p = 0.5. Conclusions On the mRNA and protein levels, OCT-4 is not differentially expressed during the menstrual cycle. Endometrial OCT-4 is not involved in or modulated by hormone-induced cyclical changes of the endometrium.
Langendijk, P.; Brand, van den H.; Gerritsen, R.; Quesnel, H.; Soede, N.M.; Kemp, B.
2008-01-01
This study presents relationships between peripheral progesterone and Insulin-like Growth Factor-1 (IGF-1) concentrations during the early luteal phase in sows. Data were derived from three experiments, one with primiparous weaned sows (n = 21) and two with multiparous sows that either ovulated
Matson, Johnny L.; Kozlowski, Alison M.
2010-01-01
Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…
Nio-Kobayashi, Junko; Boswell, Lyndsey; Amano, Maho; Iwanaga, Toshihiko; Duncan, W Colin
2014-12-01
Luteal progesterone is fundamental for reproduction, but the molecular regulation of the corpus luteum (CL) in women remains unclear. Galectin-1 and galectin-3 bind to the sugar chains on cells to control key biological processes including cell function and fate. The expression and localization of LGALS1 and LGALS3 were analyzed by quantitative PCR and histochemical analysis, with special reference to α2,6-sialylation of glycoconjugates in carefully dated human CL collected across the menstrual cycle and after exposure to human chorionic gonadotrophin (hCG) in vivo. The effects of hCG and prostaglandin E2 on the expression of galectins and an α2,6-sialyltransferase 1 (ST6GAL1) in granulosa lutein cells were analyzed in vitro. Galectin-1 was predominantly localized to healthy granulosa lutein cells and galectin-3 was localized to macrophages and regressing granulosa lutein cells. Acute exposure to luteotrophic hormones (hCG and prostaglandin E2) up-regulated LGALS1 expression (P progesterone synthesis. Luteotrophic hormones differentially regulate galectin-1 and galectin-3/α2,6-sialylation in granulosa lutein cells, suggesting a novel galectin switch regulated by luteotrophic stimuli during luteolysis and luteal rescue.
Olive, David J
2017-01-01
This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...
Role of P2X7 on steroid synthesis in murine luteal cells
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Chunping Zhang
2016-03-01
Full Text Available The extracellular adenosine triphosphate (ATP regulates different cellular functions through activating purinergic receptors as a signalling molecule or neurotransmitter. P2X7 is highly expressed in murine small luteal cells. In this study, murine luteal cells were cultured in vitro and treated with P2X7 agonists – ATP and 2′(3′-O-(4-benzoyl-benzoyl-adenosine 50-triphosphate (BzATP and with P2X7 antagonist – brilliant blue G (BBG. We found that ATP and BzATP increased the production of progesterone and had no influence on the production of estradiol. BBG reversed the effect of BzATP and ATP. Further studies demonstrated that ATP and BzATP promoted the expression of CYP11A. These results revealed that P2X7 receptor activation is involved in the steroid synthesis in corpus luteum.
International Nuclear Information System (INIS)
Rajan, V.P.; Menon, K.M.
1985-01-01
Cells isolated from superovulated rat ovaries metabolize low density lipoprotein (LDL) and high density lipoprotein (HDL) of human or rat origin and use the lipoprotein-derived cholesterol as a precursor for progesterone production. Under in vitro conditions, both lipoproteins are internalized and degraded in the lysosomes, although degradation of HDL is of lower magnitude than that of LDL. In this report we have examined the role of cellular microtubules in the internalization and degradation of human LDL and HDL in cultured rat luteal cells. The microtubule depolymerizing agents colchicine, podophyllotoxin, vinblastine, and nocodazole as well as taxol, deuterium oxide, and dimethyl sulfoxide, which are known to rapidly polymerize cellular tubulin into microtubules, were used to block the function of microtubules. When these antimicrotubule agents were included in the incubations, degradation of the apolipoproteins of [ 125 I]iodo-LDL and [ 125 I]iodo-HDL by the luteal cells was inhibited by 50-85% compared to untreated control values. Maximum inhibitory effects were observed when the cells were preincubated with the inhibitor for at least 4 h at 37 C before treatment with the labeled lipoprotein. Lipoprotein-stimulated progesterone production by luteal cells was also inhibited by 50% or more in the presence of antimicrotubule agents. However, basal and hCG-stimulated progesterone production were unaffected by these inhibitors. The binding of [ 125 I]iodo-LDL and [ 125 I]iodo-HDL to luteal cell plasma membrane receptors was not affected by the microtubule inhibitors. Although binding was unaffected and degradation was impaired in the presence of the inhibitors, there was no detectable accumulation of undegraded lipoprotein within the cells during the 24 h of study
Geber, Selmo; Moreira, Ana Carolina Ferreira; de Paula, Sálua Oliveira Calil; Sampaio, Marcos
2007-02-01
The use of progesterone for luteal phase support has been demonstrated to be beneficial in assisted reproduction cycles using gonadotrophin-releasing hormone analogues (GnRHa). Two micronized progesterone preparations are available for vaginal administration: capsules and gel. The objective of this study was to compare the efficacy of these two forms for luteal phase support in assisted reproduction cycles. A total of 244 couples undergoing IVF/intracytoplasmic sperm injection cycles were included in the study and were randomly allocated (sealed envelopes) into two groups: group 1 (122) received vaginal capsules of 200 mg of micronized progesterone (Utrogestan), 3 times daily, and group 2 (122) received micronized progesterone in gel (Crinone 8%), once daily. Both groups received progesterone for 13 days beginning day 1 after oocyte retrieval, continuing until the pregnancy test was performed and until 12 weeks of pregnancy. Groups were compared by clinical data and assisted reproduction results and had similar ages and causes of infertility. Although the pregnancy rate was higher for those receiving progesterone gel than capsules (44.26 and 36.06% respectively), this difference was not statistically significant. The study showed that vaginal progesterone gel and capsules used for luteal phase support in assisted reproduction cycles with long protocol GnRHa result in similar pregnancy rates.
DEFF Research Database (Denmark)
Iliodromiti, Stamatina; Lan, Vuong Thi Ngoc; Tuong, Ho Manh
2013-01-01
Conventional luteal support packages are inadequate to facilitate a fresh transfer after GnRH agonist (GnRHa) trigger in patients at high risk of developing ovarian hyperstimulation syndrome (OHSS). By providing intensive luteal-phase support with oestradiol and progesterone satisfactory implanta......Conventional luteal support packages are inadequate to facilitate a fresh transfer after GnRH agonist (GnRHa) trigger in patients at high risk of developing ovarian hyperstimulation syndrome (OHSS). By providing intensive luteal-phase support with oestradiol and progesterone satisfactory...... implantation rates can be sustained. The objective of this study was to assess the live-birth rate and incidence of OHSS after GnRHa trigger and intensive luteal steroid support compared to traditional hCG trigger and conventional luteal support in OHSS high risk Asian patients....
Kafi, Mojtaba; Tamadon, Amin; Saeb, Mehdi
2015-05-01
The aims of the present study were to initially determine the pattern of serum adiponectin concentrations during a normal estrous cycle in high-producing postpartum dairy cows and then evaluate the relationship between the serum concentrations of adiponectin and insulin with the commencement of postpartum luteal activity and ovarian activities in clinically healthy high-producing Holstein dairy cows. During a normal estrous cycle of cows (n = 6), serum adiponectin concentrations gradually decreased (P Cows with higher peak of milk yield had lower serum adiponectin concentrations by week 7 postpartum (P = 0.01). Serum adiponectin and insulin concentrations in cows with different postpartum luteal activity (based on the progesterone profile) were evaluated using the following class of cows: normal (≤45 days, n = 11) and delayed (>45 days, n = 11) commencement of luteal activity (C-LA) and four different profiles of normal luteal activity (NLA, n = 5), prolonged luteal phase (n = 6), delayed first ovulation (n = 6), and anovulation (AOV, n = 5). Serum adiponectin concentrations decreased gradually by week 3 postpartum in NLA and then increased; whereas in AOV and delayed first ovulation, they were decreased after week 3 postpartum (P cows was more than that of NLA cows. Insulin concentrations were almost maintained at a stable level in NLA cows (P > 0.05), whereas they increased in the other groups (P cows with C-LA greater than 45 days decreased more than those with C-LA 45 days or less after week 3 postpartum (P = 0.002). Serum adiponectin concentrations at week 7 postpartum were lower in delayed C-LA (P = 0.01). Milk yield in cows with C-LA greater than 45 days increased more than cows with C-LA 45 days or less postpartum (P = 0.002). Insulin concentrations increased relatively in parallel from weeks 1 to 7 postpartum in cows either with C-LA greater than 45 or with C-LA 45 days or less. We showed for the first time the profile of serum adiponectin concentrations
Zhang, Hongmei; Wang, Wei; Jiang, Zhenzhou; Shang, Jing; Zhang, Luyong
2010-01-01
Although Interferon-alpha (IFN-alpha, CAS 9008-11-1) is a powerful drug in treating several viral infections and certain tumors, a considerable amount of neuropsychiatric side-effects such as depression and anxiety are an unavoidable consequence. Combination with the selective serotonin (5-HT) reuptake inhibitor (SSRI) fluoxetine (CAS 56296-78-7) significantly improved the situation. However, the potential 5-HT(1A) receptor- and 5-HT(1B) receptor-signals involved in the antidepressant effects are still unclear. The effects of 5-HT(1A) receptor- and 5-HT(1B) receptor signals were analyzed by using the mouse forced swimming test (FST), a predictive test of antidepressant-like action. The present results indicated that (1) fluoxetine (administrated intragastrically, 30 mg/kg; not subactive dose: 15 mg/kg) significantly reduced IFN-alpha-induced increase of the immobility time in the forced swimming test; (2) 5-HT(1A) receptor- and 5-HT(1B) receptor ligands alone or in combination had no effects on IFN-alpha-induced increase of the immobility time in the FST; (3) surprisingly, WAY 100635 (5-HT(1A) receptor antagonist, 634908-75-1) and 8-OH-DPAT(5-HT(1A) receptor agonist, CAS 78950-78-4) markedly enhanced the antidepressant effect of fluoxetine at the subactive dose (15 mg/kg, i. g.) on the IFN-alpha-treated mice in the FST. Further investigations showed that fluoxetine combined with WAY 100635 and 8-OH-DPAT failed to produce antidepressant effects in the FST. (4) Co-application of CGS 12066A (5-HT(1B) receptor agonist, CAS 109028-09-3) or GR 127935 (5-HT(1B/1D) receptor antagonist, CAS 148642-42-6) with fluoxetine had no synergistic effects on the IFN-alpha-induced increase of immobility time in FST. (5) Interestingly, co-administration of GR 127935, WAY 100635 and fluoxetine significantly reduced the IFN-alpha-induced increase in immobility time of FST, being more effective than co-administration of WAY 100635 and fluoxetine. All results suggest that (1) compared to
IL-17A acts via p38 MAPK to increase stability of TNF-alpha-induced IL-8 mRNA in human ASM.
Henness, Sheridan; van Thoor, Eveline; Ge, Qi; Armour, Carol L; Hughes, J Margaret; Ammit, Alaina J
2006-06-01
Human airway smooth muscle (ASM) plays an immunomodulatory role in asthma. Recently, IL-17A has become of increasing interest in asthma, being found at elevated levels in asthmatic airways and emerging as playing an important role in airway neutrophilia. IL-17A predominantly exerts its neutrophil orchestrating role indirectly via the induction of cytokines by resident airway structural cells. Here, we perform an in vitro study to show that although IL-17A did not induce secretion of the CXC chemokine IL-8 from ASM cells, IL-17A significantly potentiates TNF-alpha-induced IL-8 protein secretion and gene expression in a concentration- and time-dependent manner (P ASM cells, acting via a p38 MAPK-dependent posttranscriptional pathway to augment TNF-alpha-induced secretion of the potent neutrophil chemoattractant IL-8 from ASM cells.
2008-11-19
Microbiology . All Rights Reserved. Hantaan Virus Nucleocapsid Protein Binds to Importin Proteins and Inhibits Tumor Necrosis Factor Alpha-Induced...Division, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland 21702,1 and Department of Microbiology , Mount Sinai...34–36. 32. Prescott , J., C. Ye, G. Sen, and B. Hjelle. 2005. Induction of innate immune response genes by Sin Nombre hantavirus does not require
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Anita J. Crompton
2017-11-01
Full Text Available In this work, a robust stand-off alpha detection method using the secondary effects of alpha radiation has been sought. Alpha particles ionise the surrounding atmosphere as they travel. Fluorescence photons produced as a consequence of this can be used to detect the source of the alpha emissions. This paper details experiments carried out to detect this fluorescence, with the focus on photons in the ultraviolet C (UVC wavelength range (180–280 nm. A detector, UVTron R9533 (Hamamatsu, 325-6, Sunayama-cho, Naka-ku, Hamamatsu City, Shizuoka Pref., 430-8587, Japan, designed to detect the UVC emissions from flames for fire alarm purposes, was tested in various gas atmospheres with a 210Po alpha source to determine if this could provide an avenue for stand-off alpha detection. The results of the experiments show that this detector is capable of detecting alpha-induced air fluorescence in normal indoor lighting conditions, as the interference from daylight and artificial lighting is less influential on this detection system which operates below the UVA and UVB wavelength ranges (280–315 nm and 315–380 nm respectively. Assuming a standard 1 r 2 drop off in signal, the limit of detection in this configuration can be calculated to be approximately 240 mm, well beyond the range of alpha-particles in air, which indicates that this approach could have potential for stand-off alpha detection. The gas atmospheres tested produced an increase in the detector count, with xenon having the greatest effect with a measured 52% increase in the detector response in comparison to the detector response in an air atmosphere. This type of alpha detection system could be operated at a distance, where it would potentially provide a more cost effective, safer, and faster solution in comparison with traditional alpha detection methods to detect and characterise alpha contamination in nuclear decommissioning and security applications.
Bhat, G. K.; Yang, H.; Sridaran, R.
2001-01-01
The purpose of this study was to assess whether simulated conditions of microgravity induce changes in the production of progesterone by luteal cells of the pregnant rat ovary using an in vitro model system. The microgravity environment was simulated using either a high aspect ratio vessel (HARV) bioreactor with free fall or a clinostat without free fall of cells. A mixed population of luteal cells isolated from the corpora lutea of day 8 pregnant rats was attached to cytodex microcarrier beads (cytodex 3). These anchorage dependent cells were placed in equal numbers in the HARV or a spinner flask control vessel in culture conditions. It was found that HARV significantly reduced the daily production of progesterone from day 1 through day 8 compared to controls. Scanning electron microscopy showed that cells attached to the microcarrier beads throughout the duration of the experiment in both types of culture vessels. Cells cultured in chamber slide flasks and placed in a clinostat yielded similar results when compared to those in the HARV. Also, when they were stained by Oil Red-O for lipid droplets, the clinostat flasks showed a larger number of stained cells compared to control flasks at 48 h. Further, the relative amount of Oil Red-O staining per milligram of protein was found to be higher in the clinostat than in the control cells at 48 h. It is speculated that the increase in the level of lipid content in cells subjected to simulated conditions of microgravity may be due to a disruption in cholesterol transport and/or lesions in the steroidogenic pathway leading to a fall in the synthesis of progesterone. Additionally, the fall in progesterone in simulated conditions of microgravity could be due to apoptosis of luteal cells.
Impact of peak/mid luteal estradiol on pregnancy outcome after intracytoplasmic sperm injection
International Nuclear Information System (INIS)
Rehman, R.; Hussain, Z.; Zahir, H.
2014-01-01
Objective: To compare peak to mid estradiol ratio with the probability of successful conception after intra-cytoplasmic sperm injection. Method: The quasi-experimental study was conducted in an infertility clinic at Islamabad from June 2010 till August 2011, and comprised couples subjected to intra-cytoplasmic sperm injection. Down-regulation of ovaries was followed by calculated stimulation, ovulation induction, oocytes retrieval, intra cytoplasmic sperm injection, in vitro maturation of embryos and finally blastocysts transfer. Serum estradiol was measured by enzyme-linked immunosorbent assay on ovulation induction day and the day of embryo transfer. Failure of procedure was detected by beta human chorionic gonadotropin 5-25mIU/ml (Group I; non-pregnant). Females with beta human chorionic gonadotropin>25mIU/ml and no cardiac activity after 4 weeks of transfer were placed in Group II (pre-clinical abortion), and confirmation of foetal heart in the latter comprised Group III (clinical pregnancy). Data was analysed using SPSS 15. Results: Of the 323 couples initially enrolled, embryo transfer was carried out in 282(87.3%) females. Clinical pregnancy was achieved in 101(36%) of the cases, while 61(21.63%) had pre-clinical abortion, and 120(42%) remained non-pregnant. The peak/mid-luteal estradiolratio was low (2.3) in patients who had high oocyte maturity (p=0.001) and fertilisation rate (p=0.003) compared to non-pregnant patients with high peak/mid-luteal estradiolratio (2.56). Conclusion: High peak estradiol with maintenance of optimal levels in mid-luteal phase is required for implantation of fertilised ovum and accomplishment of clinical pregnancy. (author)
Chouhan, V S; Dangi, S S; Gupta, M; Babitha, V; Khan, F A; Panda, R P; Yadav, V P; Singh, G; Sarkar, M
2014-08-01
The objectives of the present study were to investigate the effects of vascular endothelial growth factor (VEGF) on progesterone (P4) synthesis in cultured luteal cells from different stages of the estrous cycle and on expression of steroidogenic acute regulatory protein (STARD1), cytochrome P450 cholesterol side chain cleavage (CYP11A1) and 3β-hydroxysteroid dehydrogenase (HSD3B), antiapoptotic gene PCNA, and proapoptotic gene BAX in luteal cells obtained from mid-luteal phase (MLP) of estrous cycle in buffalo. Corpus luteum samples from the early luteal phase (ELP; day 1st-4th; n=4), MLP (day 5th-10th; n=4), and the late luteal phase (LLP; day 11th-16th; n=4) of oestrous cycle were obtained from a slaughterhouse. Luteal cell cultures were treated with VEGF (0, 1, 10 and 100 ng/ml) for 24, 48 and 72h. Progesterone was assessed by RIA, while mRNA expression was determined by quantitative real-time PCR (qRT-PCR). Results indicated a dose- and time-dependent stimulatory effect of VEGF on P4 synthesis and expression of steroidogenic enzymes. Moreover, VEGF treatment led to an increase in PCNA expression and decrease in BAX expression. In summary, these findings suggest that VEGF acts locally in the bubaline CL to modulate steroid hormone synthesis and cell survivability, which indicates that this factor has an important role as a regulator of CL development and function in buffalo. Copyright © 2014 Elsevier B.V. All rights reserved.
Protective role of melatonin in progesterone production by human luteal cells.
Taketani, Toshiaki; Tamura, Hiroshi; Takasaki, Akihisa; Lee, Lifa; Kizuka, Fumie; Tamura, Isao; Taniguchi, Ken; Maekawa, Ryo; Asada, Hiromi; Shimamura, Katsunori; Reiter, Russel J; Sugino, Norihiro
2011-09-01
This study investigated whether melatonin protects luteinized granulosa cells from reactive oxygen species (ROS) as an antioxidant to enhance progesterone production in the follicle during ovulation. Follicular fluid was sampled at the time of oocyte retrieval in women undergoing in vitro fertilization and embryo transfer (IVF-ET). Melatonin concentrations in the follicular fluid were positively correlated with progesterone concentrations (r = 0.342, P progesterone and 8-OHdG concentrations were negatively correlated (r = -0.246, P Progesterone production by luteinized granulosa cells was significantly inhibited by H(2)O(2). Melatonin treatment overcame the inhibitory effect of H(2) O(2) . Twenty-five patients who had luteal phase defect (serum progesterone concentrations progesterone concentrations (>10 ng/mL during the mid-luteal phase) in nine of 14 women (64.3%), whereas only two of 11 women (18.1%) showed normal serum progesterone levels in the control group. In conclusion, melatonin protects granulosa cells undergoing luteinization from ROS in the follicle and contributes to luteinization for progesterone production during ovulation. © 2011 John Wiley & Sons A/S.
Differentiating regressed melanoma from regressed lichenoid keratosis.
Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A
2017-04-01
Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Geber, Selmo; Sampaio, Marcos
2013-06-01
The effect of long-acting GnRHa, in the luteal phase, during ART cycles varies from one patient to another. The aim of this study was to evaluate whether the effect of long-acting GnRHa in the luteal phase, in ART cycles, affects pregnancy rates according to the duration of its action in such phase. This is a retrospective study of 367 patients submitted to ovulation induction for in vitro fertilization/intracytoplasmic sperm injection procedures that used long-acting depot GnRHa for pituitary suppression. Patients were stratified according to the period of action of the agonist in the luteal phase: group 1, ≤ 6 days; group 2, 7 to 12 days; and group 3, >12 days. The following variables were analyzed: ovarian response, age, infertility causes and pregnancy rates. Group 1 (n = 53) had a mean age of 33.8 ± 4.55 years (23-44 years) and a pregnancy rate of 45.2%. In group 2 (n = 118), mean age was 33.7 ± 4.5 years (24-44 years) and the pregnancy rate was 38.9%. In group 3 (n = 196), mean age was 33.7 ± 4.4 years (23-43 years) and the pregnancy rate was 47.4%. Regardless of the duration of depot GnRHa action in the luteal phase, no significant association with pregnancy rates was found.
Administration of single-dose GnRH agonist in the luteal phase in ICSI cycles: a meta-analysis
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Oliveira João
2010-09-01
Full Text Available Abstract Background The effects of gonadotrophin-releasing hormone agonist (GnRH-a administered in the luteal phase remains controversial. This meta-analysis aimed to evaluate the effect of the administration of a single-dose of GnRH-a in the luteal phase on ICSI clinical outcomes. Methods The research strategy included the online search of databases. Only randomized studies were included. The outcomes analyzed were implantation rate, clinical pregnancy rate (CPR per transfer and ongoing pregnancy rate. The fixed effects model was used for odds ratio. In all trials, a single dose of GnRH-a was administered at day 5/6 after ICSI procedures. Results All cycles presented statistically significantly higher rates of implantation (P Conclusions These findings demonstrate that the luteal-phase single-dose GnRH-a administration can increase implantation rate in all cycles and CPR per transfer and ongoing pregnancy rate in cycles with GnRH antagonist ovarian stimulation protocol. Nevertheless, by considering the heterogeneity between the trials, it seems premature to recommend the use of GnRH-a in the luteal phase. Additional randomized controlled trials are necessary before evidence-based recommendations can be provided.
Bouman, A.; Moes, H; Heineman, MJ; De Leij, LFMH; Faas, MM
PROBLEM: The aim of this study was to test the hypothesis that, during luteal phase of the ovarian cycle, as compared with follicular phase, the cytokine productive capacity of peripheral natural killer (NK)-lymphocytes in humans is shifted towards a "Th2-type"-like response. METHOD OF STUDY:
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Brezina Paul R
2012-02-01
Full Text Available Abstract Background To assess the impact of luteal phase support on the expression of estrogen receptor (ER alpha and progesterone receptors B (PR-B on the endometrium of oocyte donors undergoing controlled ovarian hyperstimulation (COH. Methods A prospective, randomized study was conducted in women undergoing controlled ovarian hyperstimulation for oocyte donation. Participants were randomized to receive no luteal support, vaginal progesterone alone, or vaginal progesterone plus orally administered 17 Beta estradiol. Endometrial biopsies were obtained at 4 time points in the luteal phase and evaluated by tissue microarray for expression of ER alpha and PR-B. Results One-hundred and eight endometrial tissue samples were obtained from 12 patients. No differences were found in expression of ER alpha and PR-B among all the specimens with the exception of one sample value. Conclusions The administration of progesterone during the luteal phase of COH for oocyte donor cycles, either with or without estrogen, does not significantly affect the endometrial expression of ER alpha and PR.
Arikan, Sevket; Yigit, Ayse Arzu
2009-10-01
The present study was designed to incubate luteal cells isolated from pseudopregnant cats and to investigate the effects of cholesterol and cAMP on luteal progesterone production. Corpora lutea were collected from the cats on days 10 and 15 of pseudopregnancy. Luteal cells were isolated from the ovaries by collagenase digestion. Steroidogenic luteal cells were stained for 3beta-hydroxysteroid dehydrogenase (3beta-HSD) activity. Cells (2 x 10(4)) staining positive for 3beta-HSD were cultured for up to 7 days. The cells were treated with 22(R)-hydroxycholesterol (22R-HC) and dibutyryl cyclic AMP (dbcAMP) on days 1, 3 and 7. Treatment of cells with 22R-HC resulted in a dose-dependent increase (pprogesterone production. When 22R-HC was used at a concentration of 10 microg/ml, it resulted in 2.7- and 5.1-fold increases in progesterone production on days 3 and 5, respectively. When the dose was doubled (20 microg/ml), treated cells produced four times more progesterone on days 3 and 7, and three times more on day 5. By day 7, progesterone production increased up to 9.1 times more than the control. Incubation of cells with both concentrations of dbcAMP (0.1 mM and 1 mM) resulted in significant stimulations of progesterone on days 5 and 7 (pProgesterone production was increased up to 2- and 2.9-fold of the control when cells were treated with lower concentration of dbcAMP (0.1 mM) on days 5 and 7, respectively. Incubation of cells with 1 mM concentrations of dbcAMP induced a 3.2-fold increase on day 5 and a 5-fold increase on day 7. In conclusion, a successful incubation was performed for long-life culturing of luteal cells collected from pseudopregnant cats. The method works well and allows for optimal growth and development of cells in the culture. The present study also demonstrated that incubating cat luteal cells with 22R-HC and dbcAMP induces a significant increase in luteal progesterone synthesis.
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
Zhao, Wei; Liu, Yifeng; Xu, Peng; Wu, Yiqing; Chen, Kai; Guo, Xiaoyan; Zhang, Fan; Huang, Yun; Zhu, Linlin; Zhang, Runjv; Zhang, Dan
2018-05-12
Any benefit of oestradiol supplementation with progesterone for luteal support after fresh embryo transfer in in vitro fertilisation/intracytoplasmic sperm injection (IVF/ICSI) cycles remains controversial. In this study, we further addressed this question in cycles using gonadotropin-releasing hormone (GnRH) agonist for ovarian stimulation. A retrospective cohort study in a tertiary teaching and research hospital. A total of 1602 patients were given oestradiol valerate (E) in addition to progesterone (P) as luteal support. One thousand six hundred and two patients receiving progesterone alone were selected as the control group. Live birth rate. Secondary measures included clinical pregnancy rate, miscarriage rate and premature birth rate. Clinical pregnancy and live birth rates were similar for the P alone vs the P+E group. In cycles with oestradiol (E2) levels less than 5000 pmol/L on the day of hCG trigger, E supplementation resulted in a significantly higher live birth rate (23.44% vs 32.92%, OR = 1.60 [95% CI 1.05 to 2.46]). In cycles with oestradiol levels 5000 to 10 000 pmol/L on the day of hCG trigger, E supplementation did not increase the live birth rate (34.43% vs 35.42%, OR = 0.90 [95% CI 0.80 to 1.01]). In cycles with oestradiol levels over 10 000 pmol/L on the day of hCG trigger, the live birth rate was significantly lower (36.83% vs 31.37%, OR = 0.78 [95% CI 0.62 to 0.99]) and the premature birth rate was significantly higher (19.66% vs 28.73%,OR = 1.65 [95% CI 1.05 to 2.59]) in the E supplementation group. Any benefit of oestradiol supplementation for luteal phase support appears to correlate with the serum oestradiol level on the day of hCG trigger. Oestradiol supplementation is beneficial for improving live birth rate in cycles with oestradiol levels less than 5000 pmol/L, but is not recommended in cycles with oestradiol levels over 10 000 pmol/L. © 2018 John Wiley & Sons Ltd.
Milewicz, A; Gejdel, E; Sworen, H; Sienkiewicz, K; Jedrzejak, J; Teucher, T; Schmitz, H
1993-07-01
The efficacy of a Vitex agnus castus preparation (Strotan capsules) was investigated in a randomized double blind study vs. placebo. This clinical study involved 52 women with luteal phase defects due to latent hyperprolactinaemia. The daily dose was one capsule (20 mg) Vitex agnus castus preparation and placebo, respectively. Aim of the study was to prove whether the elevated pituitary prolactin reserve can be reduced and deficits in luteal phase length and luteal phase progesterone synthesis be normalized. Blood for hormonal analysis was taken at days 5-8 and day 20 of the menstrual cycle before and after three month of therapy. Latent hyperprolactinaemia was analysed by monitoring the prolactin release 15 and 30 min after i.v. injection of 200 micrograms TRH. 37 complete case reports (placebo: n = 20, verum: n = 17) after 3 month of therapy were statistically evaluated. The prolactin release was reduced after 3 months, shortened luteal phases were normalised and deficits in the luteal progesterone synthesis were eliminated. These changes were significant and occurred only in the verum group. All other hormonal parameters did not change with the exception of 17 beta-estradiol which rouse up in the luteal phase in patients receiving verum. Side effects were not seen, two women treated with the Vitex agnus castus preparation got pregnant. The tested preparation is thought to be an efficient medication in the treatment of luteal phase defects due to latent hyperprolactinaemia.
Samarütel, Jaak; Waldmann, Andres; Ling, Katri; Jaakson, Hanno; Kaart, Tanel; Leesmäe, Andres; Kärt, Olav
2008-11-01
The objective was to compare the relationships between luteal activity and fertility, and relate these parameters to metabolic indices and body condition changes in multiparous Estonian Holstein cows on two commercial dairy farms under different management and levels of production and nutrition (higher, H, n=54 (71 lactations) and lower, L, n=39 (39 lactations)). For statistical analysis cows were categorized according to their milk progesterone (P4) profiles as follows: normal ovarian function; delayed start of cyclicity (DC) (interval from calving to first luteal response (P45 ng/ml up to and more than 50 d respectively, followed by regular cyclicity); cessation of luteal activity (prolonged interluteal interval, P4bodies, non-esterified fatty acids, total cholesterol) and aspartate aminotransferase, body condition scores (BCS) and fertility parameters between the two farms, and also fertility parameters between the farms within P4 categories. Differences in milk fat/protein ratio, ketone body levels and BCS indicated a deeper negative energy balance (NEB) during the first month after calving on farm L. On both farms nearly 50% of the recently calved dairy cows suffered from ovarian dysfunction during the post-partum period. Delayed start of cyclicity was the most prevalent abnormal P4 profile, 25% and 28% on farms H and L, respectively. Prolonged luteal activity accounted for one-third of atypical ovarian patterns on farm H, and cessation of luteal activity on farm L. On farm L, DC cows had lower BCS values from day 10 to day 90 after calving compared with normal cows (Pcows lost more BCS (1.2 units) during the 40 d after calving than normal resumption cows (0.75 units; P<0.05). On farm H with moderate NEB the delayed start of ovulation post partum did not impair subsequent reproductive performance.
Micro-dose hCG as luteal phase support without exogenous progesterone administration
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Andersen, C Yding; Fischer, R; Giorgione, V
2016-01-01
RHa trigger to induce ovulation showed that exogenous progesterone administration without hCG supplementation was insufficient to obtain satisfactory pregnancy rates. This has prompted development of alternative strategies for LPS. Augmenting the local endogenous production of progesterone by the multiple......For the last two decades, exogenous progesterone administration has been used as luteal phase support (LPS) in connection with controlled ovarian stimulation combined with use of the human chorionic gonadotropin (hCG) trigger for the final maturation of follicles. The introduction of the Gn...... corpora lutea has been one focus with emphasis on one hand to avoid development of ovarian hyper-stimulation syndrome and, on the other hand, to provide adequate levels of progesterone to sustain implantation. The present study evaluates the use of micro-dose hCG for LPS support and examines the potential...
Effect of Estradiol Prescribed during Luteal Phase of Art Cycles and Pregnancy Outcome
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M Karimzadeh
2007-01-01
Full Text Available Introduction: Implantation is one of the most important steps in ART cycles and it depends upon embryo and endometrial reception. Different protocols have been suggested for getting better endometrium. It seems estrogen increases the endometrial reception and pregnancy rate by inducing changes in the hormonal status. The aim of this study was to evaluate the effect of estradiol(E2 on luteal phase support and pregnancy rate in ART cycles Methods: This prospective randomized study was done in Yazd at the IVF center from March until December, 2002. 68 patients who had undergone IVF or ICSI were enrolled in the study. Exclusion criteria was age>40, endometriosis and ovarian hyper stimulation syndrome. Induction ovulation protocol was long suppression with GnRH analogues.After embryo transfer, patients were divided in two groups randomly. Both groups received 100mg progesterone IM daily from the transfer day. Estradiol valerate 2 mg/day was added from the 7th transfer day to progesterone in Group I and continued if the BhCG became positive. Abortion and malformations were measured in all patients. Data analyzed with SPSS 11.0 and P value <0.05 considered statistically significant. Results: Pregnancy rate in the 34 patients of estradiol group (group I was 26.5%which was significantly higher than 11.8 %( 4 cases in the other group (Pvalue=0.034. Abortion rate was higher in estradiol group (3 cases, but there was no abortion in the progesterone group(P=0.119. 2 cases of major fetal malformations were observed in E2 supplementation group (P=0.246 . Conclusions: E2 suplementation to progesterone in the luteal phase of ART cycles, especially in the long GnRH analogues causes higher endometrial receptivity and pregnancy rate.
Factors related to declining luteal function in women during the menopausal transition.
Santoro, N; Crawford, S L; Lasley, W L; Luborsky, J L; Matthews, K A; McConnell, D; Randolph, J F; Gold, E B; Greendale, G A; Korenman, S G; Powell, L; Sowers, M F; Weiss, G
2008-05-01
Reproductive hormones are incompletely characterized during the menopause transition (MT). Increased anovulation and decreased progesterone accompany progress through the MT. The Daily Hormone Study (DHS) of the Study of Women's Health Across the Nation (SWAN) included 848 women aged 43-53 yr at baseline who collected daily urine for one cycle or up to 50 d annually for 3 yr. LH, FSH, estrone conjugates, and pregnanediol glucuronide levels were assessed. Cycles were classified by presumed luteal (ovulatory) status and bleeding. Hormones were related to time in study, age, menopausal status, and selected variables. Ovulatory-appearing cycles declined from 80.9% at baseline to 64.7% by the third assessment (H3). Cycles presumed anovulatory and not ending with bleeding by 50 d (anovulatory/nonbleeding) increased from 8.4 to 24% by H3 and were associated with progress to early perimenopause [odds ratio (OR) = 2.66; confidence interval (CI) = 1.17-6.04] or late perimenopause (OR = 56.21; CI = 18.79-168.12; P school education (OR = 3.51; CI = 1.62-7.62). Anovulatory cycles ending with bleeding remained at about 10% from baseline to H3; compared with ovulatory cycles, they were associated with obesity (OR = 4.68; CI = 1.33-16.52) and more than high school education (OR = 2.12; CI = 1.22-3.69; P = 0.02). Serum estradiol in both the highest and lowest categories was associated with anovulatory/nonbleeding collections. Pregnanediol glucuronide decreased 6.6% for each year on study. Insulin sensitivity measures did not relate strongly to menstrual cycle hormones. Anovulation without bleeding represents progression of the MT. A small but detectable decrease in luteal progesterone excretion occurs as women progress through the MT.
Nowak, Judyta; Borkowska, Barbara; Pawlowski, Boguslaw
2016-09-10
Total leukocyte count (white blood cells-WBC) and the count of each subpopulation vary across the menstrual cycle, but results of studies examining the time and direction of these changes are inconsistent and methodologically flawed. Besides, no previous study focused on leukocyte count on the day of ovulation. Blood samples were obtained from 37 healthy and regularly cycling women aged 19.8-36.1 years. Samples were taken three times: during menstruation (M), ovulation (O), and in the mid-luteal phase (ML). WBC, neutrophils, lymphocytes, mixed cells, progesterone (P,) and estradiol (E) were measured in each of the three target phases of the cycle. Compared to menstruation, WBC (P = 0.002) and neutrophils (P < 0.001) increased around ovulation and remained stable in the mid-luteal phase, whereas lymphocyte and mixed cell counts did not change throughout the menstrual cycle. There were some correlations of sex hormone variation with leukocyte changes between M and O (positive for E and WBC, negative for P and WBC and for P and neutrophil count; P < 0.05), but not between O and ML. Peripheral leukocyte changes taking place in the second half of the cycle are already observable on the day of ovulation and they are associated with sex hormone variation. We speculate that these changes may lead to increased immune protection against pathogens at a time when fertilization and implantation typically occur. Am. J. Hum. Biol. 28:721-728, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
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Li-Jung Wang
2009-12-01
Conclusion: Luteal phase support with Crinone 8% vaginal gel (90 mg daily resulted in better clinical pregnancy and implantation rates than Utrogestan vaginal capsules (200 mg, 4 times daily in IVF/ICSI–BT cycles.
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Iliodromiti, Stamatina; Blockeel, Christophe; Tremellen, Kelton P
2013-01-01
Are clinical pregnancy rates satisfactory and the incidence of OHSS low after GnRH agonist trigger and modified intensive luteal support in patients with a high risk of ovarian hyperstimulation syndrome (OHSS)?......Are clinical pregnancy rates satisfactory and the incidence of OHSS low after GnRH agonist trigger and modified intensive luteal support in patients with a high risk of ovarian hyperstimulation syndrome (OHSS)?...
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Johansen, Søren
2008-01-01
The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...
DiLuigi, Andrea J; Engmann, Lawrence; Schmidt, David W; Benadiva, Claudio A; Nulsen, John C
2011-06-30
We performed a randomized trial to compare IVF outcomes in 54 poor responder patients undergoing a microdose leuprolide acetate (LA) protocol or a GnRH antagonist protocol incorporating a luteal phase E(2) patch and GnRH antagonist in the preceding menstrual cycle. Cancellation rates, number of oocytes retrieved, clinical pregnancy rates (PR), and ongoing PRs were similar between the two groups. Copyright © 2011 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.
Hommeida, Abdelrahim; Nakao, Toshihiko; Kubota, Hirokazu
2005-10-01
The incidence of different types of luteal activity postpartum and their effect on reproductive performance were studied in 21 postpartum dairy cows. Progesterone concentrations in defatted milk collected 3 times a week were determined by EIA. Reproductive tract examination was undertaken every other week postpartum. Body weight and body condition score (BCS) were measured before and after calving and the average 100-day milk yield was calculated. Nine (42.9%) cows had normal ovarian activity (first luteal activity or = 20 days pre-service) and in 7 (33.3%) cows the first luteal activity was shown later than 50 days postpartum (DOV). When compared with normal cows, both PLP and DOV had longer interval to first insemination (63.1 +/- 22.0 days versus 77.6 +/- 21.6 and 93.0 +/- 22.3 days, Pconception rate (88.9% versus 0.0% and 57.1%, P<0.01 and P<0.05, respectively) and greater BCS loss (0.81 +/- 0.2 versus 1.05 +/- 0.21 and 1.04 +/- 0.10, respectively, P<0.01). Cows with PLP showed longer interval to uterine involution than normal and DOV groups (54.0 +/- 8.3 days versus 42.4 +/- 5.5 and 43.3 +/- 8.3 days, respectively, P<0.01) and higher 100-day milk yield (38.8 +/- 2.7 kg versus 33.6 +/- 4.7 and 29.9 +/- 6.1 kg, respectively, P<0.01). In conclusion, more than half of the cows had abnormal luteal activity postpartum, which adversely affected reproductive performance.
Alviggi, C; Marci, R; Vallone, R; Conforti, A; Di Rella, F; Strina, I; Picarelli, S; De Rosa, P; De Laurentiis, M; Yding Andersen, C; De Placido, G
2017-07-01
To evaluate the hormonal profile in three breast cancer patients who underwent controlled ovarian stimulation in the presence of the aromatase inhibitor letrozole. In IVF University referral center, a case series of three breast cancer patients who underwent controlled ovarian stimulation (COS) with recombinant FSH and letrozole were investigated. Ovulation was induced with hCG (case No. 1) or with GnRH agonist (case No. 2-3). The primary outcome of our study was the detection of progesterone levels in the luteal phase. Very high progesterone values (mean 186.6 ± 43.6 ng/mL) during the luteal phase were recorded in all three cases. High progesterone levels can be related to the use of letrozole independently of the most commonly used trigger regimen. Although progesterone has long been considered a protective factor against breast cancer, several studies have demonstrated that progesterone could expand a transformation-sensitive stem cell population in the mammary glands. The estrogen negative feedback effect on the hypothalamus-pituitary axis and the disruption of steroid biosynthesis and could represent an intriguing reason behind this phenomenon. Our results highlight the need to evaluate further the increase in progesterone levels in the luteal phase in women with breast cancer undergoing COS with letrozole.
Rego, M F; Navarrete, M A L H; Facina, G; Falzoni, R; Silva, R; Baracat, E C; Nazario, A C P
2009-04-01
Fibroadenoma is the most common benign mammary condition among women aged 35 or younger. Expression of Ki-67 antigen has been used to compare proliferative activity of mammary fibroadenoma epithelium in the follicular and luteal phases of the menstrual cycle. Ninety eumenorrheic women were selected for tumour excision; they were assigned to either of the two groups, according to their phase of menstrual cycle. At the end of the study, 75 patients with 87 masses were evaluated by epithelial cell Ki-67 expression, blind (no information given concerning group to which any lesion belonged). Both groups were found to be homogeneous relative to age, menarche, body mass index, previous gestation, parity, breastfeeding, number of fibroadenomas, family history of breast cancer and tabagism. Median tumour size was 2.0 cm and no relationship between proliferative activity and nodule diameter was observed. No typical pattern was observed in the expression of Ki-67 in distinct nodules of the same patient. Average values for expression of Ki-67 (per 1000 epithelial cells) in follicular and luteal phases were 27.88 and 37.88, respectively (P = 0.116). Our findings revealed that proliferative activities in the mammary fibroadenoma epithelium did not present a statistically significant difference in the follicular and luteal phases. The present study contributes to clarifying that fibroadenoma is a neoplasm and does not undergo any change in the proliferative activity during the menstrual cycle.
Effect of anabolics on bovine granulosa-luteal cell primary cultures.
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Bartolomeo Biolatti
2007-10-01
Full Text Available Granulosa cell tumours are observed with increased frequency among calves slaughtered in Northern Italy. The use of illegal anabolics in breeding was taken into account as a cause of this pathology. An in vitro approach was used to detect the possible alterations of cell proliferation induced by anabolics on primary cultures of bovine granulosa-luteal cells. Cultures were treated with different concentrations of substances illegally used in cattle (17beta-estradiol, clenbuterol and boldione. Cytotoxicity was determined by means of MTT test, to exclude toxic effects induced by anabolics and to determine the highest concentration to be tested. Morphological changes were evaluated by means of routine cytology, while PCNA expression was quantified in order to estimate cell proliferation. Cytotoxic effects were revealed at the highest concentrations. The only stimulating effect on cell proliferation was detected in boldione treated cultures: after 48 h treated cells, compared to controls, showed a doubled expression of PCNA. In clenbuterol and 17beta-estradiol treated cells PCNA expression was similar to controls or even decreased. As the data suggest an alteration in cell proliferation, boldione could have a role in the early stage of pathogenesis of granulosa cell tumour in cattle.
The cytoskeleton proteins and LH-regulated steroidogenesis in porcine luteal cells
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Gregoraszczuk, Ewa L.; Slomczynska, Maria
1996-01-01
The involvement of microtubules (MT) and microflilaments (MF) in LH-regulation of luteal cell stereoidogenesis was assessed at the middle stage of corpus luteum development. The influence microtubule- and microfilament-altering agents on basal and LH-stimulated progesterone (P4) production and secretion into the incubation medium was determined by RIA. LH-stimulated P4 production was 2.5 times higher than in the control cultures. Cytochalasis B (Cyt B) was without effect on basal P4 synthesis but increased the basal fraction of P4 secreted into the incubation medium, while colchicine (Col) increased both basal P4 synthesis and the fraction of P4 secreted into the incubation medium. LH-stimulated progesterone synthesis was reduced by Col, but the fraction secreted into the incubation medium increased. Cyt B had no effect on LH-stimulated synthesis but it decreased the fraction of P4 secreted into the incubation medium. Our findings demonstrate significant differences in the effect of Cyt B and Col on steroidogenesis in corpus luteum. We conclude that microtubules play an important role in the process of LH-stimulated P4 synthesis, while microfilaments act in the process of basal and LH-stimulated P4 secretion. (author). 23 refs, 4 figs
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Telleria Carlos M
2005-08-01
Full Text Available Abstract Background In pregnant rats, structural luteal regression takes place after parturition and is associated with cell death by apoptosis. We have recently shown that the hormonal environment is responsible for the fate of the corpora lutea (CL. Changing the levels of circulating hormones in post-partum rats, either by injecting androgen, progesterone, or by allowing dams to suckle, was coupled with a delay in the onset of apoptosis in the CL. The objectives of the present investigation were: i to examine the effect of exogenous estradiol on apoptosis of the rat CL during post-partum luteal regression; and ii to evaluate the post-partum luteal expression of the estrogen receptor (ER genes. Methods In a first experiment, rats after parturition were separated from their pups and injected daily with vehicle or estradiol benzoate for 4 days. On day 4 post-partum, animals were sacrificed, blood samples were taken to determine serum concentrations of hormones, and the ovaries were isolated to study apoptosis in situ. In a second experiment, non-lactating rats after parturition received vehicle, estradiol benzoate or estradiol benzoate plus bromoergocryptine for 4 days, and their CL were isolated and used to study apoptosis ex vivo. In a third experiment, we obtained CL from rats on day 15 of pregnancy and from non-lactating rats on day 4 post-partum, and studied the expression of the messenger RNAs (mRNAs encoding the ERalpha and ERbeta genes. Results Exogenous administration of estradiol benzoate induced an increase in the number of apoptotic cells within the CL on day 4 post-partum when compared with animals receiving vehicle alone. Animals treated with the estrogen had higher serum prolactin and progesterone concentrations, with no changes in serum androstenedione. Administration of bromoergocryptine blocked the increase in serum prolactin and progesterone concentrations, and DNA fragmentation induced by the estrogen treatment. ERalpha and
Regression analysis by example
Chatterjee, Samprit
2012-01-01
Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded
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Fitzenberger, Bernd; Wilke, Ralf Andreas
2015-01-01
if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...... by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...
INFLUENCE OF EMBRYO IMPLANTATION ON ENDOMETRIUM IN LUTEAL PHASE OF MENSTRUAL CYCLE
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Romana Dmitrović
2018-02-01
Full Text Available Background: Based on the facts known from embryology, rapid endometrial growth during late luteal phase of the cycle could be expected. In this research, we sought to establish if normal intrauterine pregnancy could be confirmed before gestational sac vizualization, by trans- vaginal ultrasound and hormonal tests. The primary hypothesis was that the endometrial thickness and/or volume in the luteal phase of the cycle, in cycles resulting in normal intra- uterine pregnancy, is significantly different compared to non-conception cycles. We also hypothesized that endometrial thickness and/or volume are different in cycles resulting in normal intrauterine pregnancy compared to cycles resulting in abnormal pregnancy, namely biochemical and ectopic pregnancy, and spontaneous abortion. Additionally, next to endometrial volumes, we decided to measure the endometrium in three planes (thick- ness, length and width, to see if the hypothesized endometrial volume differences could be approximated by this simple surrogate technique, which is available in most parts of the world. Methods: This was a prospective observational study of women enrolled in an assisted reproduction program. Patients were stimulated with standard stimulation protocols. The oocyte retrieval was performed 36 hours after the hCG administration and the embryo was transferred 3 or 5 days later. Patients were first seen on day 20–24 of the cycle , and then on day 27–30 of the cycle. A blood sample was taken, and 3D transvaginal ultrasound was done. Following the completion of study visits, patients with a positive HCG test received phone call check- ups until week 12 of pregnancy, and were stratified according to pregnancy outcome. Results: 80 subjects signed the informed consent form. 4 patients had the IUI in the stimulated cycle, one had ET in spontaneous cycle, and 74 patients had undergone IVF/ET in the stimulated cycle. 63 patients in the stimulated cycles completed the study and
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Szafranska, B.; Przala, J.; Grazul-Bilska, A.
1992-01-01
The study was performed using luteal tissue obtained from 24 pregnant gilts. Group 1 was treated with bromocriptine (BR) from 37th to 42nd day of day of pregnancy. Group 2 was treated with homologous anti-pLH serum from 37th to 42nd day of pregnancy. Group 3 was given BR from 67th to 72nd day of gestation. Group 4 received anti-pLH serum from 67th to 72nd day of pregnancy. The effect of exogenous LH or prolactin (100 ng/ml) on secretion of progesterone (P 4 ) and estradiol (E 2 ) by luteal tissue was studied using perfusion technique. Prolactin caused a significant (P 4 secretion by luteal tissue from gilts in groups 1 and 4. Both LH and prolactin decreased (P 4 and E 2 secretion by luteal tissue from gilts from groups 4 and 2, respectively. The results demonstrate that both LH and prolactin have a regulatory role in steroid secretion by luteal tissue of gilts in the mid- and late period of pregnancy. (author). 28 refs, 2 figs
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Nintasen, Rungrat [Division of Cardiovascular Medicine, Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, Leeds LS2 9JT (United Kingdom); Multidisciplinary Cardiovascular Research Center (MCRC), University of Leeds, Leeds LS2 9JT (United Kingdom); Department of Tropical Pathology, Faculty of Tropical Medicine, Mahidol University (Thailand); Riches, Kirsten; Mughal, Romana S. [Division of Cardiovascular Medicine, Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, Leeds LS2 9JT (United Kingdom); Multidisciplinary Cardiovascular Research Center (MCRC), University of Leeds, Leeds LS2 9JT (United Kingdom); Viriyavejakul, Parnpen; Chaisri, Urai; Maneerat, Yaowapa [Department of Tropical Pathology, Faculty of Tropical Medicine, Mahidol University (Thailand); Turner, Neil A. [Division of Cardiovascular Medicine, Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, Leeds LS2 9JT (United Kingdom); Multidisciplinary Cardiovascular Research Center (MCRC), University of Leeds, Leeds LS2 9JT (United Kingdom); Porter, Karen E., E-mail: medkep@leeds.ac.uk [Division of Cardiovascular Medicine, Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, Leeds LS2 9JT (United Kingdom); Multidisciplinary Cardiovascular Research Center (MCRC), University of Leeds, Leeds LS2 9JT (United Kingdom)
2012-04-20
Highlights: Black-Right-Pointing-Pointer TNF-{alpha} augments neointimal hyperplasia in human saphenous vein. Black-Right-Pointing-Pointer TNF-{alpha} induces detrimental effects on endothelial and smooth muscle cell function. Black-Right-Pointing-Pointer Estradiol exerts modulatory effects on TNF-induced vascular cell functions. Black-Right-Pointing-Pointer The modulatory effects of estradiol are discriminatory and cell-type specific. -- Abstract: Coronary heart disease (CHD) is a condition characterized by increased levels of proinflammatory cytokines, including tumor necrosis factor-{alpha} (TNF-{alpha}). TNF-{alpha} can induce vascular endothelial cell (EC) and smooth muscle cell (SMC) dysfunction, central events in development of neointimal lesions. The reduced incidence of CHD in young women is believed to be due to the protective effects of estradiol (E2). We therefore investigated the effects of TNF-{alpha} on human neointima formation and SMC/EC functions and any modulatory effects of E2. Saphenous vein (SV) segments were cultured in the presence of TNF-{alpha} (10 ng/ml), E2 (2.5 nM) or both in combination. Neointimal thickening was augmented by incubation with TNF-{alpha}, an effect that was abolished by co-culture with E2. TNF-{alpha} increased SV-SMC proliferation in a concentration-dependent manner that was optimal at 10 ng/ml (1.5-fold increase), and abolished by E2 at all concentrations studied (1-50 nM). Surprisingly, E2 itself at low concentrations (1 and 5 nM) stimulated SV-SMC proliferation to a level comparable to that of TNF-{alpha} alone. SV-EC migration was significantly impaired by TNF-{alpha} (42% of control), and co-culture with E2 partially restored the ability of SV-EC to migrate and repair the wound. In contrast, TNF-{alpha} increased SV-SMC migration by 1.7-fold, an effect that was completely reversed by co-incubation with E2. Finally, TNF-{alpha} potently induced ICAM-1 and VCAM-1 expression in both SV-EC and SV-SMC. However there
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Thor Haahr
2017-06-01
Full Text Available IntroductionThe use of GnRH agonist (GnRHa for final oocyte maturation trigger in oocyte donation and elective frozen embryo transfer cycles is well established due to lower ovarian hyperstimulation syndrome (OHSS rates as compared to hCG trigger. A recent Cochrane meta-analysis concluded that GnRHa trigger was associated with reduced live birth rates (LBRs in fresh autologous IVF cycles compared to hCG trigger. However, the evidence is not unequivocal, and recent trials have found encouraging reproductive outcomes among couples undergoing GnRHa trigger and individualized luteal LH activity support. Thus, the aim was to compare GnRHa trigger followed by luteal LH activity support with hCG trigger in IVF patients undergoing fresh embryo transfer.Material and methodsWe conducted a systematic review and meta-analysis of randomized trials published until December 14, 2016. The population was infertile patients submitted to IVF/ICSI cycles with GnRH antagonist cotreatment who underwent fresh embryo transfer. The intervention was GnRHa trigger followed by LH activity luteal phase support (LPS. The comparator was hCG trigger followed by a standard LPS. The critical outcome measures were LBR and OHSS rate. The secondary outcome measures were number of oocytes retrieved, clinical and ongoing pregnancy rates, and miscarriage rates.ResultsA total of five studies met the selection criteria comprising a total of 859 patients. The LBR was not significantly different between the GnRHa and hCG trigger groups (OR 0.84, 95% CI 0.62, 1.14. OHSS was reported in a total of 4/413 cases in the GnRHa group compared to 7/413 in the hCG group (OR 0.48, 95% CI 0.15, 1.60. We observed a slight, but non-significant increase in miscarriage rate in the GnRHa triggered group compared to the hCG group (OR 1.85; 95% CI 0.97, 3.54.ConclusionGnRHa trigger with LH activity LPS resulted in comparable LBRs compared to hCG trigger. The most recent trials reported LBRs close to unity
Understanding logistic regression analysis
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...
Introduction to regression graphics
Cook, R Dennis
2009-01-01
Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava
Alternative Methods of Regression
Birkes, David
2011-01-01
Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s
GARCIA-ISPIERTO, Irina; LÓPEZ-GATIUS, Fernando
2014-01-01
This study compares in two experiments the responses of lactating dairy cows to four different progesterone-based protocols for fixed-time artificial insemination (FTAI) in terms of their effects on follicular/luteal dynamics and fertility. The protocols consisted of a progesterone intravaginal device fitted for five days, along with the administration of different combinations of gonadotropin releasing hormone, equine chorionic gonadotropin and a single or double dose (24 h apart) of prostaglandin F2α. In Experiment I, the data were derived from 232 lactating cows. Binary logistic regression identified no effects of treatment on ovulation failure or multiple ovulation 10 days post artificial insemination (AI). Based on the odds ratio, the likelihood of ovulation failure was lower (by a factor of 0.1) in cows showing at least one corpus luteum (CL) upon treatment than in cows lacking a CL; repeat breeders (> 3 AI) and cows with multiple CLs at treatment showed lower (by a factor of 0.44) and higher (by a factor of 9.0) risks of multiple ovulation, respectively, than the remaining animals. In Experiment II, the data were derived from 5173 AIs. The independent variable treatment failed to affect the conception rate 28–34 days post AI, twin pregnancy or early fetal loss 58–64 days post AI. The results of this study demonstrate the efficacy of 5-day progesterone-based protocols for FTAI. All four protocols examined were able to induce ovulation in both cyclic and non-cyclic animals so that FTAI returned a similar pregnancy rate to spontaneous estrus. Our results suggest that the ovarian response and fertility resulting from each treatment are due more to the effect of ovarian structures at treatment than to the different combinations of hormones investigated. PMID:25196275
Garcia, J A; Ferreira, H L; Vieira, F V; Gameiro, R; Andrade, A L; Eugênio, F R; Flores, E F; Cardoso, T C
2017-06-01
Oncolytic virotherapy is a novel strategy for treatment of cancer in humans and companion animals as well. Canine distemper virus (CDV), a paramyxovirus, has proven to be oncolytic through induction of apoptosis in canine-derived tumour cells, yet the mechanism behind this inhibitory action is poorly understood. In this study, three human mammary tumour cell lines and one canine-derived adenofibrosarcoma cell line were tested regarding to their susceptibility to CDV infection, cell proliferation, apoptosis, mitochondrial membrane potential and expression of tumour necrosis factor-alpha-induced protein 8 (TNFAIP8). CDV replication-induced cytopathic effect, decrease of cell proliferation rates, and >45% of infected cells were considered death and/or under late apoptosis/necrosis. TNFAIP8 and CDVM gene expression were positively correlated in all cell lines. In addition, mitochondrial membrane depolarization was associated with increase in virus titres (p < 0.005). Thus, these results strongly suggest that both human and canine mammary tumour cells are potential candidates for studies concerning CDV-induced cancer therapy. © 2015 John Wiley & Sons Ltd.
Wang, T; Xu, X; Xu, Q; Ren, J; Shen, S; Fan, C; Hou, Y
2017-06-08
Chronic inflammation is believed to have a crucial role in colon cancer development. MicroRNA (miRNA) deregulation is common in human colorectal cancers, but little is known regarding whether miRNA drives tumor progression by regulating inflammation. Here, we showed that miR-19a can promote colitis and colitis-associated colon cancer (CAC) development using a CAC mouse model and an acute colitis mouse model. Tumor necrosis factor-α (TNF-α) stimulation can increase miR-19a expression, and upregulated miR-19a can in turn activate nuclear factor (NF)-κB signaling and TNF-α production by targeting TNF alpha-induced protein 3 (TNFAIP3). miR-19a inhibition can also alleviate CAC in vivo. Moreover, the regulatory effects of miR-19a on TNFAIP3 and NF-κB signaling were confirmed using tumor samples from patients with colon cancer. These new findings demonstrate that miR-19a has a direct role in upregulating NF-κB signaling and that miR-19a has roles in inflammation and CAC.
Fraser, H M; Lunn, S F; Kim, H; Duncan, W C; Rodger, F E; Illingworth, P J; Erickson, G F
2000-04-01
In the human menstrual cycle, extensive angiogenesis accompanies luteinization; and the process is physiologically important for corpus luteum (CL) function. During luteolysis, the vasculature collapses, and the endothelial cells die. In a conceptual cycle, the CL persists both functionally and structurally beyond the luteoplacental shift. Although luteal rescue is not associated with increased angiogenesis, endothelial survival is extended. Despite the central role of the luteal vasculature in fertility, the mechanisms regulating its development and demise are poorly understood. There is increasing evidence that insulin-like growth factors (IGFs) and their binding proteins (IGFBPs) may be important effectors of luteal function. Here, we have found that IGFBP-3 messenger RNA is expressed in the endothelium of the human CL and that the levels of message change during luteal development and rescue by human CG. The signal was strong during the early luteal phase, but it showed significant reduction during the mid- and late luteal phases. Interestingly, administration of human CG caused a marked increase in the levels of IGFBP-3 messenger RNA in luteal endothelial cells that was comparable to that observed during the early luteal phase. We conclude that endothelial cell IGFBP-3 expression is a physiological property of the CL of menstruation and pregnancy. These observations raise the intriguing possibility that the regulated expression of endothelial IGFBP-3 may play a role in controlling angiogenesis and cell responses in the human CL by autocrine/paracrine mechanisms.
Directory of Open Access Journals (Sweden)
Matthias Schmid
Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.
Understanding logistic regression analysis.
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.
Weisberg, Sanford
2013-01-01
Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus
Hosmer, David W; Sturdivant, Rodney X
2013-01-01
A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-
Directory of Open Access Journals (Sweden)
Teplitz MA
2016-12-01
Full Text Available The aim of this study was to establish and characterize the porcine luteal cells (PLC culture for the subsequent coculture with porcine COC. The final purpose is to promote the oocyte maturation. The PLC was established using corpora lutea obtained from slaughterhouse ovaries. Corpora lutea were dissected and luteal tissue submitted to a mechanical and enzymatic digestion with collagenase IV. The cell suspension was filtered and centrifuged and the cells obtained were diluted in 15 mL of DMEM-F12 supplemented media. Diluted cells were seeded in 3 culture flasks T25, staying in a controlled environment and changing the medium every 2 days. For the analysis and characterization, the cells were assessed by the Nile red staining to detect intracellular lipids, immunocytochemistry (ICC for 3β-hydroxy steroid dehidrogenase (3β-HSD and ELISA for P4 determination. We observed the presence of lipid intracellular droplets. Also, we observed an increase of P4 concentration at 48, 96 y 144 h of primary culture and almost all the cells were positive to the ICC evaluation for 3β-HSD, showing the steroidogenic capacity of the culture cells.
Understanding poisson regression.
Hayat, Matthew J; Higgins, Melinda
2014-04-01
Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.
International Nuclear Information System (INIS)
Rome, S.P.; Karamoskos, P.; Schlicht, S.M.
2003-01-01
Full text: The incidence of hepatitis C virus (HCV) infection is increasing. Interferon alpha therapy is often used to treat patients who are HCV positive. Thyroid gland autoimmunity and dysfunction has been reported to occur with variable frequency during INF-alpha therapy in patients with the HCV. This study reviews the scintigraphic findings of thyroid scans in such patients in order to assess for the effects on thyroid scintigraphy. To our knowledge, there has been no comprehensive study of this important occurrence to date. There were a number of patients with the HCV being treated at our institution between 23/09/1996 and 09/08/2000. Some of them received INF-alpha therapy, certain were subsequently diagnosed with thyroid gland autoimmunity and/or dysfunction. Eight were imaged with thyroid scintigraphy and reviewed. The scintigraphic findings in the 8 patients fell into two broad categories; 4 demonstrated changes of Graves' disease, and 3 changes of thyroiditis (1 of these was sub-acute). One hypothyroid patient with anti-thyroglobulin antibodies had normal thyroid scintigraphy. Six patients were found to have antithyroid antibodies. One patient with thyroiditis tested negative to antithyroid antibodies. One patient was not tested for antithyroid antibodies. Interferon alpha induced thyroid gland autoimmunity and/or dysfunction can markedly affect the thyroid scintigraphic findings of patients with the hepatitis C virus. This hitherto undescribed occurrence on thyroid scintigraphy has important practical implications of which Nuclear Medicine Specialists need to be aware in order to correctly interpret thyroid scintigraphy studies in such patients. The clinical presentation and effects on imaging appearances are varied. The Nuclear Medicine Specialist can play a central role in establishing the causal link. Awareness of this occurrence enables the Nuclear Medicine Specialist to add value to the referral. This occurrence will become an increasingly common
Directory of Open Access Journals (Sweden)
Mok Tik
2014-06-01
Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.
Multicollinearity and Regression Analysis
Daoud, Jamal I.
2017-12-01
In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.
DEFF Research Database (Denmark)
Kol, Shahar; Humaidan, Peter; Itskovitz-Eldor, Joseph
2011-01-01
in normal responder IVF patients. We here present a novel approach for luteal support after a GnRHa trigger. METHODS Normal responder patients who failed at least one previous IVF attempt, during which a conventional hCG trigger was used, were consecutively enrolled in the study. A GnRH antagonist...
DEFF Research Database (Denmark)
Bache, Stefan Holst
A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....
DEFF Research Database (Denmark)
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface...... for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by...... functionals. The software presented here is implemented in the riskRegression package....
Dunne, Caitlin; Seethram, Ken; Roberts, Jeffrey
2015-09-01
Growth hormone (GH) acts in both early and late follicular development to stimulate the proliferation and differentiation of granulosa cells and to increase the production of estradiol in animal and human ovaries. Investigators have therefore explored GH supplementation to improve outcomes in women undergoing in vitro fertilization, with the greatest interest in women with diminished ovarian reserve. Recent meta-analyses indicate that GH supplementation can be beneficial for poor responders undergoing IVF. In most studies, GH has been given concomitantly with gonadotropins during the follicular phase; this may not be optimal, since follicular recruitment begins during the preceding luteal phase. We therefore wished to examine the effect of GH supplementation in the luteal phase before controlled ovarian stimulation (COH) with a microdose GnRH agonist flare (MDF) protocol in women undergoing in vitro fertilization. We performed a retrospective matched case-control study of patients undergoing treatment at a private IVF facility between June 2012 and July 2013. Patients identified as poor responders to COH were offered adjuvant GH treatment as part of their ovarian stimulation regimen. The patients in the experimental group chose to take GH, 3.33 mg daily by subcutaneous injection for 14 days, before starting COH. All patients had an MDF stimulation protocol using 450 IU of follicle stimulating hormone (FSH) daily. A total of 42 women were included in the study. There were 14 women in the experimental group (GH) and 28 controls (C) matched for age, BMI, and day 3 FSH level. There was no difference between the groups in clinical pregnancy rate (GH = 29%, C = 32%, P = 0.99), number of mature oocytes retrieved (GH = 2.5, C = 5.0, P = 0.13), cycle cancellation rate (GH = 21%, C = 14%, P = 0.88), duration of COH (GH = 10.1, C = 10.1, P = 0.93), or mean peak estradiol level (GH = 4174 pmol/L, C = 5105 pmol/L, P = 0.44). The administration of growth hormone during the
Kohen, Paulina; Castro, Olga; Palomino, Alberto; Muñoz, Alex; Christenson, Lane K; Sierralta, Walter; Carvallo, Pilar; Strauss, Jerome F; Devoto, Luigi
2003-07-01
This study was designed 1) to assess corpus luteum (CL) steroidogenesis in response to exogenous human chorionic gonadotropin (hCG) at different times during the luteal phase, 2) to examine the effect of hCG on steroidogenic acute regulatory protein (StAR) expression within the CL, 3) to correlate StAR expression and luteal steroidogenic responses to hCG, and 4) to determine whether endogenous LH regulates ovarian steroidogenesis in the early luteal phase. Blood was collected before and after hCG treatment for steroid and hCGbeta determinations. CL were obtained at the time of surgery to assess StAR gene and protein expression. During the early luteal phase various women received the GnRH antagonist for 24-48 h; some of them also received hCG 24 h after the GnRH antagonist. A slight steroidogenic response to hCG was observed in early luteal phase; 17alpha-hydroxyprogesterone, but not progesterone (P4), levels were significantly increased 8 h post-hCG, indicating a differential response by the granulosa and theca-lutein cells. The 1.6- and 4.4-kb StAR transcripts and the 37-kDa preprotein and 30-kDa mature StAR protein did not change post-hCG administration in early luteal phase CL. In contrast, the StAR 4.4- and 1.6-kb transcripts diminished significantly (P < 0.05) after the antagonist treatment. Immunohistochemical staining for StAR protein was weak, particularly in granulosa-lutein cells. Treatment with hCG restored StAR mRNA and protein and plasma P4 levels within 24 h in antagonist-treated women. hCG stimulated the highest plasma concentrations of P4 and estradiol in the midluteal phase, indicating its greatest steroidogenic capacity. Midluteal tissue StAR gene and protein expression increased by 1.6- and 1.4-fold after 24 h of hCG treatment, respectively. Administration of hCG resulted in the greatest increment in plasma P4 (4-fold) and 17alpha-hydroxyprogesterone (3-fold) levels over baseline in the late luteal phase. This was associated with an increase in
Multiple linear regression analysis
Edwards, T. R.
1980-01-01
Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
Bayesian logistic regression analysis
Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.
2012-01-01
In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an
Seber, George A F
2012-01-01
Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.
Ritz, Christian; Parmigiani, Giovanni
2009-01-01
R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. This book provides a coherent treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.
Bayesian ARTMAP for regression.
Sasu, L M; Andonie, R
2013-10-01
Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.
Bounded Gaussian process regression
DEFF Research Database (Denmark)
Jensen, Bjørn Sand; Nielsen, Jens Brehm; Larsen, Jan
2013-01-01
We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework. We...... with the proposed explicit noise-model extension....
and Multinomial Logistic Regression
African Journals Online (AJOL)
This work presented the results of an experimental comparison of two models: Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) for classifying students based on their academic performance. The predictive accuracy for each model was measured by their average Classification Correct Rate (CCR).
Mechanisms of neuroblastoma regression
Brodeur, Garrett M.; Bagatell, Rochelle
2014-01-01
Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179
Rashtian, Justin; Zhang, John
2018-03-22
In older women with severe diminished ovarian response (DOR), in vitro fertilization (IVF) treatment is much less successful due to the low number of mature oocytes collected. The objective of this study was to assess whether follicular-phase stimulation (FPS) and luteal-phase stimulation (LPS) in the same menstrual cycle (double ovarian stimulation) in older women with severe DOR will produce a higher number of oocytes compared to FPS alone. Women with DOR (n = 69; mean age = 42.4) who underwent double ovarian stimulation for IVF were included. Women underwent ovarian stimulation in FPS using clomiphene citrate, letrozole, and gonadotropins followed by oocyte retrieval. The next day following oocyte retrieval, women underwent a second ovarian stimulation (LPS) using the same medications followed by a second oocyte retrieval. T-test was performed in order to compare the clinical characteristics and outcome in the same participant between FPS and LPS. Although antral follicle count at the start of FPS tended to be higher than at the start of the LPS cycle, there was no statistically significant difference between the duration of ovarian stimulation, peak estradiol levels, number of small (FPS alone. The addition of LPS to the conventional FPS increases the number of mature oocytes retrieved in the same IVF cycle, thus potentially increasing the chances of pregnancy in older women with severe DOR. AFC: antral follicle count; BMI: body mass index; DOR: diminished ovarian reserve; E2: estradiol; FPS: follicular-phase stimulation; FSH: follicle stimulating hormone; GnRH: gonadotropin-releasing hormone; HCG: human chorionic gonadotropin; IRB: institutional review board; IVF: in vitro fertilization; LH: luteinizing hormone; LPS: luteal-phase stimulation; MII: metaphase II.
Ridge Regression Signal Processing
Kuhl, Mark R.
1990-01-01
The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.
Subset selection in regression
Miller, Alan
2002-01-01
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...
Better Autologistic Regression
Directory of Open Access Journals (Sweden)
Mark A. Wolters
2017-11-01
Full Text Available Autologistic regression is an important probability model for dichotomous random variables observed along with covariate information. It has been used in various fields for analyzing binary data possessing spatial or network structure. The model can be viewed as an extension of the autologistic model (also known as the Ising model, quadratic exponential binary distribution, or Boltzmann machine to include covariates. It can also be viewed as an extension of logistic regression to handle responses that are not independent. Not all authors use exactly the same form of the autologistic regression model. Variations of the model differ in two respects. First, the variable coding—the two numbers used to represent the two possible states of the variables—might differ. Common coding choices are (zero, one and (minus one, plus one. Second, the model might appear in either of two algebraic forms: a standard form, or a recently proposed centered form. Little attention has been paid to the effect of these differences, and the literature shows ambiguity about their importance. It is shown here that changes to either coding or centering in fact produce distinct, non-nested probability models. Theoretical results, numerical studies, and analysis of an ecological data set all show that the differences among the models can be large and practically significant. Understanding the nature of the differences and making appropriate modeling choices can lead to significantly improved autologistic regression analyses. The results strongly suggest that the standard model with plus/minus coding, which we call the symmetric autologistic model, is the most natural choice among the autologistic variants.
Regression in organizational leadership.
Kernberg, O F
1979-02-01
The choice of good leaders is a major task for all organizations. Inforamtion regarding the prospective administrator's personality should complement questions regarding his previous experience, his general conceptual skills, his technical knowledge, and the specific skills in the area for which he is being selected. The growing psychoanalytic knowledge about the crucial importance of internal, in contrast to external, object relations, and about the mutual relationships of regression in individuals and in groups, constitutes an important practical tool for the selection of leaders.
Classification and regression trees
Breiman, Leo; Olshen, Richard A; Stone, Charles J
1984-01-01
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Hilbe, Joseph M
2009-01-01
This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...
Steganalysis using logistic regression
Lubenko, Ivans; Ker, Andrew D.
2011-02-01
We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.
SEPARATION PHENOMENA LOGISTIC REGRESSION
Directory of Open Access Journals (Sweden)
Ikaro Daniel de Carvalho Barreto
2014-03-01
Full Text Available This paper proposes an application of concepts about the maximum likelihood estimation of the binomial logistic regression model to the separation phenomena. It generates bias in the estimation and provides different interpretations of the estimates on the different statistical tests (Wald, Likelihood Ratio and Score and provides different estimates on the different iterative methods (Newton-Raphson and Fisher Score. It also presents an example that demonstrates the direct implications for the validation of the model and validation of variables, the implications for estimates of odds ratios and confidence intervals, generated from the Wald statistics. Furthermore, we present, briefly, the Firth correction to circumvent the phenomena of separation.
DEFF Research Database (Denmark)
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface......-product we obtain fast access to the baseline hazards (compared to survival::basehaz()) and predictions of survival probabilities, their confidence intervals and confidence bands. Confidence intervals and confidence bands are based on point-wise asymptotic expansions of the corresponding statistical...
Adaptive metric kernel regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
2000-01-01
Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...
Adaptive Metric Kernel Regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
1998-01-01
Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression...... by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...
Weems, Yoshie S; Ma, Yan; Ford, Stephen P; Nett, Terry M; Vann, Rhonda C; Lewis, Andrew W; Neuendorff, Don A; Welsh, Thomas H; Randel, Ronald D; Weems, Charles W
2014-12-01
Previously, it was reported that intraluteal implants containing prostaglandin E1 or E2 (PGE1 and PGE2) in Angus or Brahman cows prevented luteolysis by preventing loss of mRNA expression for luteal LH receptors and luteal unoccupied and occupied LH receptors. In addition, intraluteal implants containing PGE1 or PGE2 upregulated mRNA expression for FP prostanoid receptors and downregulated mRNA expression for EP2 and EP4 prostanoid receptors. Luteal weight during the estrous cycle of Brahman cows was reported to be lesser than that of Angus cows but not during pregnancy. The objective of this experiment was to determine whether intraluteal implants containing PGE1 or PGE2 alter vascular endothelial growth factor (VEGF), fibroblast growth factor-2 (FGF-2), angiopoietin-1 (ANG-1), and angiopoietin-2 (ANG-2) protein in Brahman or Angus cows. On Day 13 of the estrous cycle, Angus cows received no intraluteal implant and corpora lutea were retrieved, or Angus and Brahman cows received intraluteal silastic implants containing vehicle, PGE1, or PGE2 on Day 13 and corpora lutea were retrieved on Day 19. Corpora lutea slices were analyzed for VEGF, FGF-2, ANG-1, and ANG-2 angiogenic proteins via Western blot. Day-13 Angus cow luteal tissue served as preluteolytic controls. Data for VEGF were not affected (P > 0.05) by day, breed, or treatment. PGE1 or PGE2 increased (P Angus cows compared with Day-13 and Day-19 Angus controls but decreased (P Angus controls. There was no effect (P > 0.05) of PGE1 or PGE2 on ANG-1 in Angus luteal tissue when compared with Day-13 or Day-19 controls, but ANG-1 was decreased (P Angus Vehicle controls when compared with Day-13 Angus controls, which was prevented (P Angus cows. There was no effect (P > 0.05) of PGE1 or PGE2 on ANG-2 in Brahman cows. PGE1 or PGE2 may alter cow luteal FGF-2, ANG-1, or ANG-2 but not VEGF to prevent luteolysis; however, species or breed differences may exist. Published by Elsevier Inc.
DEFF Research Database (Denmark)
Hansen, Henrik; Tarp, Finn
2001-01-01
This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy....... investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes.......This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy...
DEFF Research Database (Denmark)
Andersen, Claus Yding; Elbaek, Helle Olesen; Alsbjerg, Birgit
2015-01-01
antagonist protocol. Two study arms were included both having 125 IU hCG daily for luteal phase support without exogenous progesterone after using a GnRHa trigger for ovulation induction. In both study arms exogenous FSH was stopped on stimulation day 6 and replaced by exogenous hCG that was initiated...... on either stimulation day 2 or day 6. Blood samples were obtained on the day of ovulation induction, on the day of oocyte pickup (OPU) and day OPU + 7. MAIN RESULTS AND THE ROLE OF CHANCE: The mean serum levels of hCG did not exceeded the normal physiological range of LH activity in any samples. Mid...... were seen between groups. LIMITATIONS, REASONS FOR CAUTION: The number of patients included is limited and conclusions need to be verified in a larger RCT. WIDER IMPLICATIONS OF THE FINDINGS: Endogenous production of progesterone may become more attractive as the luteal phase support with levels of LH...
Yıldırım, Koray; Vural, M Rıfat; Küplülü, Sükrü; Ozcan, Ziya; Polat, I Mert
2014-04-01
The objective of this study was to evaluate the influence of epidermal growth factor (EGF) and insulin like growth factor-I (IGF-1) on the in vitro maturation of cat oocytes recovered from follicular and luteal stage ovaries. Oocytes from follicular (n=580) and luteal (n=209) stages were harvested and divided into four groups, which were cultured in FSH-mediated maturation medium supplemented with: (1) EGF alone (25ng/mL); (2) IGF-1 alone (100ng/mL); (3) EGF+IGF-1 (25ng/mL EGF+100ng/mL IGF-I); or (4) no growth factor (control). The proportion of follicular stage oocytes reaching the metaphase II stage was significantly higher than that of oocytes obtained at the luteal stage in both control and study groups (pIGF-1, and 78.1% in EGF+IGF-1 groups, whereas the respective values for gametes collected from luteal stage ovaries were 12.5%, 17.5%, 12.5%, and 16.9%. Additionally, the differences between the study and control groups were significant in the case of follicular stage oocytes. Finally, supplementing the maturation medium with EGF and/or IGF-1 significantly enhanced the meiotic maturation of oocytes recovered from follicular stage ovaries. The present study also demonstrated that the combination of EGF and IGF-I provides an additional or synergic effect on meiotic maturation of oocytes recovered from the follicular stage. Copyright © 2014 Society for Biology of Reproduction & the Institute of Animal Reproduction and Food Research of Polish Academy of Sciences in Olsztyn. Published by Elsevier Urban & Partner Sp. z o.o. All rights reserved.
Modified Regression Correlation Coefficient for Poisson Regression Model
Kaengthong, Nattacha; Domthong, Uthumporn
2017-09-01
This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).
Sierralta, Walter D; Kohen, Paulina; Castro, Olga; Muñoz, Alex; Strauss, Jerome F; Devoto, Luigi
2005-10-20
The distribution of the steroidogenic acute regulatory protein (StAR) inside thecal and granulosa-lutein cells of human corpus luteum (CL) was assessed by immunoelectron microscopy. We found greater levels of StAR immunolabeling in steroidogenic cells from early- and mid-than in late luteal phase CL and lower levels in cells from women treated with a GnRH antagonist in the mid-luteal phase. Immunoelectron microscopy revealed significant levels of StAR antigen in the mitochondria and in the cytoplasm of luteal cells. The 30 kDa mature StAR protein was present in both mitochondria and cytosol (post-mitochondrial) fractions from homogenates of CL at different ages, whereas cytochrome c and mitochondrial HSP70 were detected only in the mitochondrial fraction. Therefore, we hypothesized that either appreciable processing of StAR 37 kDa pre-protein occurs outside the mitochondria, or mature StAR protein is selectively released into the cytoplasm after mitochondrial processing. The presence of mature StAR in the cytoplasm is consonant with the notion that StAR acts on the outer mitochondrial membrane to effect sterol import, and that StAR may interact with other cytoplasmic proteins involved in cholesterol metabolism, including hormone sensitive lipase.
International Nuclear Information System (INIS)
Gizewski, Elke R.; Wanke, Isabel; Forsting, Michael; Krause, Eva; Senf, Wolfgang
2006-01-01
Previous studies of gender-specific differences in functional imaging during spatial and language tasks have been inconclusive. Furthermore, among women, such differences may occur during mid-luteal phase compared to the rest of the menstrual cycle. In order to examine further gender differences, functional MRI was performed in 12 male volunteers and 12 female volunteers (in the mid-luteal phase) during mental rotation and verb-generation tests. Two-sample t-tests with uncorrected P values of <0.001 for the specific regions of interest (ROIs) revealed cerebral activation differences in both stimuli. During mental rotation tests, higher levels of activation were noted in the right medial frontal, precentral, and bilateral inferior parietal cortex, while in women this occurred in the right inferior and medial temporal, right superior frontal cortex, and left fusiform gyrus. During verb-generation tests, higher levels of activation in men was found in the left medial temporal and precentral cortex. Our results indicate that differences in cerebral activity during cognitive tasks can be shown between men and women in the mid-luteal phase. Gender differences while performing a mental rotation task were more prominent than during a verb-generation task. (orig.)
Durand, Marta; Seppala, Markku; Cravioto, Ma Del Carmen; Koistinen, Hannu; Koistinen, Riitta; González-Macedo, José; Larrea, Fernando
2005-06-01
This study examined serum glycodelin concentrations and endometrial expression during the luteal phase following oral administration of levonorgestrel (LNG) at different stages of the ovarian cycle. Thirty women were recruited and allocated into three groups. All groups were studied during two consecutive cycles, a control cycle and the treatment cycle. In the treatment cycle, each woman received two doses of 0.75 mg LNG taken 12 h apart on days 3-4 before the luteinizing hormone (LH) surge (Group 1), at the time of LH rise (Group 2) and 48 h after the rise in LH was detected (Group 3). Serum progesterone (P) and glycodelin were measured daily during the luteal phase, and an endometrial biopsy was taken at day LH +9 for immunohistochemical glycodelin-A staining. In Group 1, serum P levels were significantly lower, serum glycodelin levels rose earlier and endometrial glycodelin-A expression was weaker than in Groups 2 and 3, in which no differences were found between control and treatment cycles. Levonorgestrel taken for emergency contraception (EC) prior to the LH surge alters the luteal phase secretory pattern of glycodelin in serum and endometrium. Based on the potent gamete adhesion inhibitory activity of glycodelin-A, the results may account for the action of LNG in EC in those women who take LNG before the LH surge.
Zhou, Weiqin; Zhuang, Yanyan; Pan, Yanping; Xia, Fei
2017-05-01
To investigate the effects and safety of gonadotropin releasing hormone analogue (GnRH-a) as an addition to progesterone luteal support in women who underwent in vitro fertilization/intracytoplasmic sperm injection-embryo transfer (IVF/ICSI-ET) and achieved a clinical pregnancy. A retrospective analysis was conducted on 214 patients who underwent IVF/ICSI-ET procedures with standard long mid-luteal protocol, of which 123 received GnRH-a-free protocol and 91 received GnRH-a-added protocol. The patients' pregnancy and delivery course, and their neonates' status at birth and growth/development after birth were statistically compared. There was no significant difference between both study groups regarding embryo risks and maternal complications during early pregnancy. as well as fetal risks during the middle and late stages and neonate risks during birth, except that the twin pregnancies of the GnRH-a-added group had a considerably greater male/female ratio, and a significantly higher rate of premature delivery and low birth weight than those of the GnRH-a-free group. In addition, there was no significant difference in neonate risks within 2 years after birth between both cohorts. With precautions taken to control the number of implanted embryos and reduce the incidence of twinning pregnancy, the addition of GnRH-a to luteal support is relatively safe and effective.
Luo, Chongliang; Liu, Jin; Dey, Dipak K; Chen, Kun
2016-07-01
In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis with predictive modeling, balancing between the association strength of the canonical variates and their joint predictive power on the outcomes. Moreover, the proposed criterion seeks multiple sets of canonical variates simultaneously to enable the examination of their joint effects on the outcomes, and is able to handle multivariate and non-Gaussian outcomes. An efficient algorithm based on variable splitting and Lagrangian multipliers is proposed. Simulation studies show the superior performance of the proposed approach. We demonstrate the effectiveness of the proposed approach in an [Formula: see text] intercross mice study and an alcohol dependence study. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Meenakumari, Karukayil J; Banerjee, Arnab; Krishna, Amitabh
2009-01-01
The primary aim of this study was to determine the possible cause of slow or delayed embryonic development in Cynopterus sphinx by investigating morphological and steroidogenic changes in the corpus luteum (CL) and circulating hormone concentrations during two pregnancies of a year. This species showed delayed post-implantational embryonic development during gastrulation of the first pregnancy. Morphological features of the CL showed normal luteinization during both pregnancies. The CL did not change significantly in luteal cell size during the delay period of the first pregnancy as compared with the second pregnancy. The circulating progesterone and 17beta-estradiol concentrations were significantly lower during the period of delayed embryonic development as compared with the same stage of embryonic development during the second pregnancy. We also showed a marked decline in the activity of 3beta-hydroxysteroid dehydrogenase, P450 side chain cleavage enzyme, and steroidogenic acute regulatory peptide in the CL during the delay period. This may cause low circulating progesterone and estradiol synthesis and consequently delay embryonic development. What causes the decrease in steroidogenic factors in the CL during the period of delayed development in C. sphinx is under investigation.
Bakas, Panagiotis; Simopoulou, Maria; Giner, Maria; Drakakis, Petros; Panagopoulos, Perikles; Vlahos, Nikolaos
2017-10-01
The objective of this study is to assess if the difference of repeated measurements of estradiol and progesterone during luteal phase predict the outcome of intrauterine insemination. Prospective study. Reproductive clinic. 126 patients with infertility. Patients underwent controlled ovarian stimulation with recombinant FSH (50-150 IU/d). The day of IUI patients were given p.o natural micronized progesterone in a dose of 100 mg/tds. The area under the receiver characteristic operating curve (ROC curve) in predicting clinical pregnancy for % change of estradiol level on days 6 and 10 was 0.892 with 95% CI: 0.82-0.94. A cutoff value of change > -29.5% had a sensitivity of 85.7 with a specificity of 90.2. The corresponding ROC curve for % change of progesterone level was 0.839 with 95% CI: 0.76-0.90. A cutoff value of change > -33% had a sensitivity of 85 with a specificity of 75. The % change of estradiol and progesterone between days 6 and 10 has a predictive ability of pregnancy after IUI with COS of 89.2% and 83.4%, respectively. The addition of % of progesterone to % change of estradiol does not improve the predictive ability of % estradiol and should not be used.
Starbuck, G R; Mann, G E
2010-04-01
We have investigated the effects administering exogenous progesterone, via insertion of a controlled internal drug release (CIDR) for 4 days, from either day 5 or day 12 of the oestrous cycle on plasma oestradiol concentrations. In study 1, in which progesterone was administered from day 5, measurement of plasma oestradiol in daily samples revealed a significant (p < 0.001) decrease in peripheral oestradiol concentration. In contrast, in study 2, similar administration of progesterone from day 12 had no effect on plasma oestradiol concentration. In study 3, collection of hourly samples following progesterone treatment on day 5 revealed peak progesterone concentrations within 1 h of CIDR insertion and nadir oestradiol concentrations within 4 h. The results demonstrate that treatment with progesterone early in the luteal phase causes a rapid inhibition of oestradiol secretion, while later treatment does not. While improvements in pregnancy rate following progesterone treatment at this time have traditionally been attributed to increases in progesterone, the potential involvement of decreased oestradiol secretion has often been overlooked.
DEFF Research Database (Denmark)
Humaidan, Peter; Polyzos, N P; Alsbjerg, B
2013-01-01
, there was a lack of blinding in the RCTs. WIDER IMPLICATIONS OF THE FINDINGS: Although a non-significant result, one bolus of 1.500 IU hCG after GnRHa trigger tended to reduce the OHSS rate in patients with 15-25 follicles ≥11 mm as well as secure the ongoing pregnancy rate. In contrast, in patients at low risk......STUDY QUESTION: Does a GnRH agonist (GnRHa) trigger followed by a bolus of 1.500 IU hCG in a group of patients at risk of ovarian hyperstimulation syndrome (OHSS) reduce the OHSS incidence compared with hCG trigger? SUMMARY ANSWER: A GnRHa trigger followed by early luteal hCG support with one bolus...... of 1.500 IU hCG appears to reduce OHSS in patients at risk of OHSS; however, in a low-risk group a second bolus of 1.500 IU hCG induced two cases of late onset OHSS. WHAT IS KNOWN ALREADY: A GnRHa trigger is an alternative to hCG in GnRH antagonist co-treated cycles. STUDY DESIGN, SIZE, DURATION: Two...
Polynomial regression analysis and significance test of the regression function
International Nuclear Information System (INIS)
Gao Zhengming; Zhao Juan; He Shengping
2012-01-01
In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)
Recursive Algorithm For Linear Regression
Varanasi, S. V.
1988-01-01
Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.
Combining Alphas via Bounded Regression
Directory of Open Access Journals (Sweden)
Zura Kakushadze
2015-11-01
Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.
Regression in autistic spectrum disorders.
Stefanatos, Gerry A
2008-12-01
A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.
Linear regression in astronomy. I
Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh
1990-01-01
Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.
Advanced statistics: linear regression, part I: simple linear regression.
Marill, Keith A
2004-01-01
Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.
Anuradha; Krishna, Amitabh
2017-07-01
The aim of this study was to evaluate the role of prolactin as a modulator of luteal steroidogenesis during the period of delayed embryonic development in Cynopterus sphinx. A marked decline in circulating prolactin levels was noted during the months of November through December coinciding with the period of decreased serum progesterone and delayed embryonic development. The seasonal changes in serum prolactin levels correlated positively with circulating progesterone (P) level, but inversely with circulating melatonin level during first pregnancy showing delayed development in Cynopterus sphinx. The results also showed decreased expression of prolactin receptor-short form (PRL-RS) both in the corpus luteum and in the utero-embryonic unit during the period of delayed embryonic development. Bats treated in vivo with prolactin during the period of delayed development showed significant increase in serum progesterone and estradiol levels together with significant increase in the expression of PRL-RS, luteinizing hormone receptor (LH-R), steroidogenic acute receptor protein (STAR) and 3β-hydroxysteroid dehydrogenase (3β-HSD) in the ovary. Prolactin stimulated ovarian angiogenesis (vascular endothelial growth factor) and cell survival (B-cell lymphoma 2) in vivo. Significant increases in ovarian progesterone production and the expression of prolactin-receptor, LH-R, STAR and 3β-HSD proteins were noted following the exposure of LH or prolactin in vitro during the delayed period. In conclusion, short-day associated increased melatonin level may be responsible for decreased prolactin release during November-December. The decline in prolactin level might play a role in suppressing P and estradiol-17β (E2) estradiol levels thereby causing delayed embryonic development in C. sphinx. Copyright © 2017 Elsevier Inc. All rights reserved.
Luo, F; Jia, R; Ying, S; Wang, Z; Wang, F
2016-06-01
Nutrition is an important factor that regulates reproductive performance of sheep and affects follicle development. However, the correlation between nutrition and follicle development is poorly understood at the molecular level. To study its possible molecular mechanisms, we performed expression profiling of granulosa cells isolated from sheep that were fed different levels of nutrition levels during the luteal phase. To do this, ewes received a maintenance diet (M), and their estrus was synchronized by intravaginal progestogen sponges for 12 days. Ewes were randomly divided into the short-term dietary-restricted group (R; 0.5 × M) and the nutrient-supplemented group (S; 1.5 × M). RNA samples were extracted from granulosa cells. Transcriptome libraries from each group were constructed by Illumina sequencing. Among 18 468 detected genes, 170 genes were significantly differentially expressed, of which 140 genes were upregulated and 30 genes were downregulated in group S relative to group R. These genes could be candidates regulating follicular development in sheep. Gene Ontology, KEGG and clustering analyses were performed. Genes related to oocyte meiosis, such as ADCY7, were upregulated. We identified two important groups of related genes that were upregulated with improved nutrition: one group comprising the genes PTGS2, UCP2 and steroidogenic acute regulatory protein and the other group comprising interleukin-1A and interleukin-1B. The genes within each group showed similar expression patterns. Additionally, all five genes are involved in the reproduction process. Quantitative real-time PCR was performed to validate the results of expression profiling. These data in our study are an abundant genomic resource to expand the understanding of the molecular and cellular events underlying follicle development. © 2016 Stichting International Foundation for Animal Genetics.
Linear regression in astronomy. II
Feigelson, Eric D.; Babu, Gutti J.
1992-01-01
A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.
Time-adaptive quantile regression
DEFF Research Database (Denmark)
Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik
2008-01-01
and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power......An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....
Retro-regression--another important multivariate regression improvement.
Randić, M
2001-01-01
We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.
International Nuclear Information System (INIS)
Navarrete, Maria Alicia H; Maier, Carolina M; Falzoni, Roberto; Quadros, Luiz Gerk de Azevedo; Lima, Geraldo R; Baracat, Edmund C; Nazário, Afonso CP
2005-01-01
During the menstrual cycle, the mammary gland goes through sequential waves of proliferation and apoptosis. In mammary epithelial cells, hormonal and non-hormonal factors regulate apoptosis. To determine the cyclical effects of gonadal steroids on breast homeostasis, we evaluated the apoptotic index (AI) determined by terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) staining in human mammary epithelial cells during the spontaneous menstrual cycle and correlated it with cellular proliferation as determined by the expression of Ki-67 during the same period. Normal breast tissue samples were obtained from 42 randomly selected patients in the proliferative (n = 21) and luteal (n = 21) phases. Menstrual cycle phase characterization was based on the date of the last and subsequent menses, and on progesterone serum levels obtained at the time of biopsy. The proliferation index (PI), defined as the number of Ki-67-positive nuclei per 1,000 epithelial cells, was significantly larger in the luteal phase (30.46) than in the follicular phase (13.45; P = 0.0033). The AI was defined as the number of TUNEL-positive cells per 1,000 epithelial cells. The average AI values in both phases of the menstrual cycle were not statistically significant (P = 0.21). However, the cell renewal index (CRI = PI/AI) was significantly higher in the luteal phase (P = 0.033). A significant cyclical variation of PI, AI and CRI was observed. PI and AI peaks occurred on about the 24th day of the menstrual cycle, whereas the CRI reached higher values on the 28th day. We conclude that proliferative activity is dependent mainly on hormonal fluctuations, whereas apoptotic activity is probably regulated by hormonal and non-hormonal factors
Birse, Kenzie; Arnold, Kelly B; Novak, Richard M; McCorrister, Stuart; Shaw, Souradet; Westmacott, Garrett R; Ball, Terry B; Lauffenburger, Douglas A; Burgener, Adam
2015-09-01
The variable infectivity and transmissibility of HIV/SHIV has been recently associated with the menstrual cycle, with particular susceptibility observed during the luteal phase in nonhuman primate models and ex vivo human explant cultures, but the mechanism is poorly understood. Here, we performed an unbiased, mass spectrometry-based proteomic analysis to better understand the mucosal immunological processes underpinning this observed susceptibility to HIV infection. Cervicovaginal lavage samples (n = 19) were collected, characterized as follicular or luteal phase using days since last menstrual period, and analyzed by tandem mass spectrometry. Biological insights from these data were gained using a spectrum of computational methods, including hierarchical clustering, pathway analysis, gene set enrichment analysis, and partial least-squares discriminant analysis with LASSO feature selection. Of the 384 proteins identified, 43 were differentially abundant between phases (P HIV. Recent studies have discovered an enhanced susceptibility to HIV infection during the progesterone-dominant luteal phase of the menstrual cycle. However, the mechanism responsible for this enhanced susceptibility has not yet been determined. Understanding the source of this vulnerability will be important for designing efficacious HIV prevention technologies for women. Furthermore, these findings may also be extrapolated to better understand the impact of exogenous hormone application, such as the use of hormonal contraceptives, on HIV acquisition risk. Hormonal contraceptives are the most widely used contraceptive method in sub-Saharan Africa, the most HIV-burdened area of the world. For this reason, research conducted to better understand how hormones impact host immunity and susceptibility factors important for HIV infection is a global health priority. Copyright © 2015, American Society for Microbiology. All Rights Reserved.
Directory of Open Access Journals (Sweden)
Gazvani Rafet
2012-07-01
Full Text Available Abstract Background Luteal support with progesterone is necessary for successful implantation of the embryo following egg collection and embryo transfer in an in-vitro fertilization (IVF cycle. Progesterone has been used for as little as 2 weeks and for as long as 12 weeks of gestation. The optimal length of treatment is unresolved at present and it remains unclear how long to treat women receiving luteal supplementation. Design The trial is a prospective, randomized, double-blind, placebo-controlled trial to investigate the effect of the duration of luteal support with progesterone in IVF cycles. Following 2 weeks standard treatment and a positive biochemical pregnancy test, this randomized control trial will allocate women to a supplementary 8 weeks treatment with vaginal progesterone or 8 weeks placebo. Further studies would be required to investigate whether additional supplementation with progesterone is beneficial in early pregnancy. Discussion Currently at the Hewitt Centre, approximately 32.5% of women have a positive biochemical pregnancy test 2 weeks after embryo transfer. It is this population that is eligible for trial entry and randomization. Once the patient has confirmed a positive urinary pregnancy test they will be invited to join the trial. Once the consent form has been completed by the patient a trial prescription sheet will be sent to pharmacy with a stated collection time. The patient can then be randomized and the drugs dispensed according to pharmacy protocol. A blood sample will then be drawn for measurement of baseline hormone levels (progesterone, estradiol, free beta-human chorionic gonadotrophin, pregnancy-associated plasma protein-A, Activin A, Inhibin A and Inhibin B. The primary outcome measure is the proportion of all randomized women that continue successfully to a viable pregnancy (at least one fetus with fetal heart rate >100 beats/minute on transabdominal/transvaginal ultrasound at 10 weeks post embryo
Directory of Open Access Journals (Sweden)
Robab Davar
2015-08-01
Full Text Available Background: There is no doubt that luteal phase support is essential to enhance the reproductive outcome in IVF cycles. In addition to progesterone and human chorionic gonadotropin, several studies have described GnRH agonists as luteal phase support to improve implantation rate, pregnancy rate and live birth rate, whereas other studies showed dissimilar conclusions. All of these studies have been done in fresh IVF cycles. Objective: To determine whether an additional GnRH agonist administered at the time of implantation for luteal phase support in frozen-thawed embryo transfer (FET improves the embryo developmental potential. Materials and Methods: This is a prospective controlled trial study in 200 FET cycles, patients were randomized on the day of embryo transfer into group 1 (n=100 to whom a single dose of GnRH agonist (0.1 mg triptorelin was administered three days after transfer and group 2 (n=100, who did not receive agonist. Both groups received daily vaginal progesterone suppositories plus estradiol valerate 6 mg daily. Primary outcome measure was clinical pregnancy rate. Secondary outcome measures were implantation rate, chemical, ongoing pregnancy rate and abortion rate. Results: A total of 200 FET cycles were analyzed. Demographic data and embryo quality were comparable between two groups. No statistically significant difference in clinical and ongoing pregnancy rates was observed between the two groups (26% versus 21%, p=0.40 and 21% versus 17%, p=0.37, respectively. Conclusion: Administration of a subcutaneous GnRH agonist at the time of implantation does not increase clinical or ongoing pregnancy.
Quantile regression theory and applications
Davino, Cristina; Vistocco, Domenico
2013-01-01
A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and
Panel Smooth Transition Regression Models
DEFF Research Database (Denmark)
González, Andrés; Terasvirta, Timo; Dijk, Dick van
We introduce the panel smooth transition regression model. This new model is intended for characterizing heterogeneous panels, allowing the regression coefficients to vary both across individuals and over time. Specifically, heterogeneity is allowed for by assuming that these coefficients are bou...
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Testing discontinuities in nonparametric regression
Dai, Wenlin; Zhou, Yuejin; Tong, Tiejun
2017-01-01
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Logistic Regression: Concept and Application
Cokluk, Omay
2010-01-01
The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…
Fungible weights in logistic regression.
Jones, Jeff A; Waller, Niels G
2016-06-01
In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
International Nuclear Information System (INIS)
Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei
2007-01-01
Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age
Tumor regression patterns in retinoblastoma
International Nuclear Information System (INIS)
Zafar, S.N.; Siddique, S.N.; Zaheer, N.
2016-01-01
To observe the types of tumor regression after treatment, and identify the common pattern of regression in our patients. Study Design: Descriptive study. Place and Duration of Study: Department of Pediatric Ophthalmology and Strabismus, Al-Shifa Trust Eye Hospital, Rawalpindi, Pakistan, from October 2011 to October 2014. Methodology: Children with unilateral and bilateral retinoblastoma were included in the study. Patients were referred to Pakistan Institute of Medical Sciences, Islamabad, for chemotherapy. After every cycle of chemotherapy, dilated funds examination under anesthesia was performed to record response of the treatment. Regression patterns were recorded on RetCam II. Results: Seventy-four tumors were included in the study. Out of 74 tumors, 3 were ICRB group A tumors, 43 were ICRB group B tumors, 14 tumors belonged to ICRB group C, and remaining 14 were ICRB group D tumors. Type IV regression was seen in 39.1% (n=29) tumors, type II in 29.7% (n=22), type III in 25.6% (n=19), and type I in 5.4% (n=4). All group A tumors (100%) showed type IV regression. Seventeen (39.5%) group B tumors showed type IV regression. In group C, 5 tumors (35.7%) showed type II regression and 5 tumors (35.7%) showed type IV regression. In group D, 6 tumors (42.9%) regressed to type II non-calcified remnants. Conclusion: The response and success of the focal and systemic treatment, as judged by the appearance of different patterns of tumor regression, varies with the ICRB grouping of the tumor. (author)
Regression to Causality : Regression-style presentation influences causal attribution
DEFF Research Database (Denmark)
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...
Regression analysis with categorized regression calibrated exposure: some interesting findings
Directory of Open Access Journals (Sweden)
Hjartåker Anette
2006-07-01
Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a
Advanced statistics: linear regression, part II: multiple linear regression.
Marill, Keith A
2004-01-01
The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.
Logic regression and its extensions.
Schwender, Holger; Ruczinski, Ingo
2010-01-01
Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Vu Hai V
2012-10-01
Full Text Available Abstract Background Prostaglandin F2alpha (PGF induces luteolysis in cow by inducing a rapid reduction in progesterone production (functional luteolysis followed by tissue degeneration (structural luteolysis. However the mechanisms of action of PGF remain unclear. Reactive oxygen species (ROS play important roles in regulating the luteolytic action of PGF. The local concentration of ROS is controlled by superoxide dismutase (SOD, the main enzyme involved in the control of intraluteal ROS. Thus SOD seems to be involved in luteolysis process induced by PGF in cow. Methods To determine the dynamic relationship between PGF and ROS in bovine corpus luteum (CL during luteolysis, we determined the time-dependent change of Copper/Zinc SOD (SOD1 in CL tissues after PGF treatment in vivo. We also investigated whether PGF and hydrogen peroxide (H2O2 modulates SOD1 expression and SOD activity in cultured bovine luteal endothelial cells (LECs in vitro. Results Following administration of a luteolytic dose of PGF analogue (0 h to cows at the mid-luteal stage, the expression of SOD1 mRNA and protein, and total SOD activity in CL tissues increased between 0.5 and 2 h, but fell below the initial (0 h level at 24 h post-treatment. In cultured LECs, the expression of SOD1 mRNA was stimulated by PGF (1–10 microM and H2O2 (10–100 microM at 2 h (P
Boldt, Ariane; Becker, Frank; Martin, Gunter; Nürnberg, Gerd; Römer, Anke; Kanitz, Wilhelm
2015-06-01
The interval from calving to commencement of luteal activity (CLA) was determined by progesterone measurements from milk samples obtained once a week until the 14th week post-partum in 513 German Holstein cows in first to third parity. Milk samples were analyzed by an "on-farm" device (eProCheck(®), Minitüb, Germany) and simultaneously by RIA. The objective of this study was to examine the effect of milk yield, protein content and body condition of a cow on the CLA post-partum. Milk progesterone concentrations of "on-farm" measurements correlated with measurements done by the RIA-method significantly (r=0.72; PCows with a milk protein content at 1st milk recording of ≤3.5% revealed first luteal activity 1.3±0.3 weeks later than cows that had a content of >3.75% protein (Pcows with assisted calving or dystocia presented significantly later CLA than cows which required no help during the calving process (Pfertility cannot be confirmed regarding to CLA. The negative energy balance after calving, caused by the high milk yields, is more detrimental for the cyclical activity as was shown by the parameters milk protein content and change in BFT. Copyright © 2015 Elsevier B.V. All rights reserved.
Abstract Expression Grammar Symbolic Regression
Korns, Michael F.
This chapter examines the use of Abstract Expression Grammars to perform the entire Symbolic Regression process without the use of Genetic Programming per se. The techniques explored produce a symbolic regression engine which has absolutely no bloat, which allows total user control of the search space and output formulas, which is faster, and more accurate than the engines produced in our previous papers using Genetic Programming. The genome is an all vector structure with four chromosomes plus additional epigenetic and constraint vectors, allowing total user control of the search space and the final output formulas. A combination of specialized compiler techniques, genetic algorithms, particle swarm, aged layered populations, plus discrete and continuous differential evolution are used to produce an improved symbolic regression sytem. Nine base test cases, from the literature, are used to test the improvement in speed and accuracy. The improved results indicate that these techniques move us a big step closer toward future industrial strength symbolic regression systems.
Quantile Regression With Measurement Error
Wei, Ying; Carroll, Raymond J.
2009-01-01
. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a
From Rasch scores to regression
DEFF Research Database (Denmark)
Christensen, Karl Bang
2006-01-01
Rasch models provide a framework for measurement and modelling latent variables. Having measured a latent variable in a population a comparison of groups will often be of interest. For this purpose the use of observed raw scores will often be inadequate because these lack interval scale propertie....... This paper compares two approaches to group comparison: linear regression models using estimated person locations as outcome variables and latent regression models based on the distribution of the score....
Testing Heteroscedasticity in Robust Regression
Czech Academy of Sciences Publication Activity Database
Kalina, Jan
2011-01-01
Roč. 1, č. 4 (2011), s. 25-28 ISSN 2045-3345 Grant - others:GA ČR(CZ) GA402/09/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust regression * heteroscedasticity * regression quantiles * diagnostics Subject RIV: BB - Applied Statistics , Operational Research http://www.researchjournals.co.uk/documents/Vol4/06%20Kalina.pdf
Regression methods for medical research
Tai, Bee Choo
2013-01-01
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the
Forecasting with Dynamic Regression Models
Pankratz, Alan
2012-01-01
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
Logistic regression for dichotomized counts.
Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W
2016-12-01
Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.
Producing The New Regressive Left
DEFF Research Database (Denmark)
Crone, Christine
members, this thesis investigates a growing political trend and ideological discourse in the Arab world that I have called The New Regressive Left. On the premise that a media outlet can function as a forum for ideology production, the thesis argues that an analysis of this material can help to trace...... the contexture of The New Regressive Left. If the first part of the thesis lays out the theoretical approach and draws the contextual framework, through an exploration of the surrounding Arab media-and ideoscapes, the second part is an analytical investigation of the discourse that permeates the programmes aired...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...
Niethammer, B; Körner, C; Schmidmayr, M; Luppa, P B; Seifert-Klauss, V R
2015-12-01
Introduction: Several authors have linked subclinical ovulatory disturbances in normal length menstrual cycles to premenopausal fracture risk and bone changes. This study systematically examined the influence of ovulation and anovulation on the bone metabolism of premenopausal women. Participants and Methods: In 176 cycles in healthy premenopausal women, FSH, 17β-estradiol (E2) and progesterone (P4) as well as bone alkalic phosphatase (BAP), pyridinoline (PYD) and C-terminal crosslinks (CTX) were measured during the follicular and during the luteal phase. The probability and timing of ovulation was self-assessed by a monitoring device. In addition, bone density of the lumbar spine was measured by quantitative computed tomography (QCT) at baseline and at the end of the study. Analysis was restricted to blood samples taken more than three days before the following menstruation. Results: 118 cycles out of the 176 collected cycles were complete with blood samples taken within the correct time interval. Of these, 56.8 % were ovulatory by two criteria (ovulation symbol shown on the monitor display and LP progesterone > 6 ng/ml), 33.1 % were possibly ovulatory by one criterion (ovulation symbol shown on the monitor display or LP progesterone > 6 ng/ml), and 10.2 % were anovulatory by both criteria). Ovulation in the previous cycle and in the same cycle did not significantly influence the mean absolute concentrations of the bone markers. However, bone formation (BAP) was higher in the luteal phase of ovulatory cycles than in anovulatory cycles (n. s.) and the relative changes within one cycle were significantly different for bone resorption (CTX) during ovulatory vs. anovulatory cycles (p cycles following each other directly, both ovulation in the previous cycle and ovulation in the present cycle influenced CTX, but not the differences of other bone markers. Conclusion: Ovulatory cycles reduce bone resorption in their luteal phase and that of the
A Matlab program for stepwise regression
Directory of Open Access Journals (Sweden)
Yanhong Qi
2016-03-01
Full Text Available The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.
Correlation and simple linear regression.
Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G
2003-06-01
In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.
Regression filter for signal resolution
International Nuclear Information System (INIS)
Matthes, W.
1975-01-01
The problem considered is that of resolving a measured pulse height spectrum of a material mixture, e.g. gamma ray spectrum, Raman spectrum, into a weighed sum of the spectra of the individual constituents. The model on which the analytical formulation is based is described. The problem reduces to that of a multiple linear regression. A stepwise linear regression procedure was constructed. The efficiency of this method was then tested by transforming the procedure in a computer programme which was used to unfold test spectra obtained by mixing some spectra, from a library of arbitrary chosen spectra, and adding a noise component. (U.K.)
Nonparametric Mixture of Regression Models.
Huang, Mian; Li, Runze; Wang, Shaoli
2013-07-01
Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.
Cactus: An Introduction to Regression
Hyde, Hartley
2008-01-01
When the author first used "VisiCalc," the author thought it a very useful tool when he had the formulas. But how could he design a spreadsheet if there was no known formula for the quantities he was trying to predict? A few months later, the author relates he learned to use multiple linear regression software and suddenly it all clicked into…
Regression Models for Repairable Systems
Czech Academy of Sciences Publication Activity Database
Novák, Petr
2015-01-01
Roč. 17, č. 4 (2015), s. 963-972 ISSN 1387-5841 Institutional support: RVO:67985556 Keywords : Reliability analysis * Repair models * Regression Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.782, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/novak-0450902.pdf
Survival analysis II: Cox regression
Stel, Vianda S.; Dekker, Friedo W.; Tripepi, Giovanni; Zoccali, Carmine; Jager, Kitty J.
2011-01-01
In contrast to the Kaplan-Meier method, Cox proportional hazards regression can provide an effect estimate by quantifying the difference in survival between patient groups and can adjust for confounding effects of other variables. The purpose of this article is to explain the basic concepts of the
Kernel regression with functional response
Ferraty, Frédéric; Laksaci, Ali; Tadj, Amel; Vieu, Philippe
2011-01-01
We consider kernel regression estimate when both the response variable and the explanatory one are functional. The rates of uniform almost complete convergence are stated as function of the small ball probability of the predictor and as function of the entropy of the set on which uniformity is obtained.
DEFF Research Database (Denmark)
Bergh, Christina; Lindenberg, Svend; Al Humaidan, Peter Samir Heskjær
2012-01-01
fertility centres in Denmark and Sweden between March 2006 and January 2010. A web-based randomization program was used with concealed allocation of patients. Patients were randomized to one of two groups: vaginal progesterone gel or vaginal micronized progesterone tablets. There was no blinding of patients....... PARTICIPANTS AND SETTING: A total of 2057 women ≤ 40 years of age were included and down-regulated, using the long agonist protocol and rFSH for stimulation. Luteal support was given for 19 days after embryo transfer or until a negative pregnancy test Day 14 after embryo transfer. Patient convenience...... (pregnancy) is robust, blinding would have been unlikely to affect the results. Unfortunately, owing to an error in the randomization, the intended age distribution allocated older women to the micronized progesterone tablet group. In the analysis of results, adjustments were made for age and number...
DEFF Research Database (Denmark)
Humaidan, Peter; Bredkjær, Helle Ejdrup; Westergaard, Lars Grabow
2009-01-01
OBJECTIVE: To prospectively assess the reproductive outcome with a small bolus of hCG administered on the day of oocyte retrieval after ovulation induction with a GnRH agonist (GnRHa). DESIGN: Prospective, randomized trial. SETTING: Three hospital-based IVF clinics. PATIENT(S): Three hundred five...... IVF/intracytoplasmic sperm injection patients after a GnRH antagonist protocol. INTERVENTION(S): Ovulation induction was performed with either 10,000 IU hCG or 0.5 mg GnRHa (buserelin) supplemented with 1,500 IU hCG on the day of oocyte retrieval. MAIN OUTCOME MEASURE(S): Reproductive outcome...... bolus of hCG in the GnRHa group secured the luteal phase, resulting in a comparable reproductive outcome in the two groups. However, a nonsignificant difference of 7% in delivery rates justifies further studies to refine the use of GnRHa for ovulation induction....
Šuluburić, Adam; Milanović, Svetlana; Vranješ-Đurić, Sanja; Jovanović, Ivan B; Barna, Tomislav; Stojić, Milica; Fratrić, Natalija; Szenci, Ottó; Gvozdić, Dragan
2017-09-01
Early embryonic development may be negatively affected by insufficient progesterone (P4) production. Therefore, the aim of our study was to increase P4 by gonadotropin-releasing hormone (GnRH) and/or human chorionic gonadotropin (hCG) treatments after inducing oestrus by prostaglandin (PG) treatment. Lactating Simmental dairy cows (n = 110), between 1 to 5 lactations, with an average milk production of 6,500 1/305 days, at 40-80 days postpartum were used and grouped as follows: (1) PG + GnRH treatment at AI (GnRH group), (2) PG + hCG treatment at day 7 after AI (hCG group), (3) PG + GnRH at AI + hCG treatment at day 7 after AI (GnRH/hCG group), and (4) spontaneous oestrus (C: control group). All animals were double inseminated (at the time of oestrus detection and 12 ± 2 h thereafter). Blood serum and milk samples were collected at the day of observed oestrus (day 0), and 14, 21 and 28 days after AI. Serum P4 was determined using a commercial radioimmunoassay (RIA) test (INEP, Zemun), and milk P4 was determined using enzyme-linked immunoassay (ELISA) test (NIV Novi Sad). Pregnancy status was confirmed by ultrasonography between days 28 and 35 after AI. Differences of serum or milk P4 medians, pregnancy (and calving) rate were determined using Dunn's Multiple Comparison Tests and Z test, respectively. Serum P4 medians were significantly higher at days 14, 21 and 28 after AI in the hCG-treated animals, indicating increased luteal activity, with a similar tendency in whole milk P4 values. Treatment with hCG during the early luteal phase significantly contributed to the maintenance of gestation at days 28-35 after AI, and also increased the calving rate in Simmental dairy cows.
Quantile Regression With Measurement Error
Wei, Ying
2009-08-27
Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.
Multivariate and semiparametric kernel regression
Härdle, Wolfgang; Müller, Marlene
1997-01-01
The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...
Regression algorithm for emotion detection
Berthelon , Franck; Sander , Peter
2013-01-01
International audience; We present here two components of a computational system for emotion detection. PEMs (Personalized Emotion Maps) store links between bodily expressions and emotion values, and are individually calibrated to capture each person's emotion profile. They are an implementation based on aspects of Scherer's theoretical complex system model of emotion~\\cite{scherer00, scherer09}. We also present a regression algorithm that determines a person's emotional feeling from sensor m...
Directional quantile regression in R
Czech Academy of Sciences Publication Activity Database
Boček, Pavel; Šiman, Miroslav
2017-01-01
Roč. 53, č. 3 (2017), s. 480-492 ISSN 0023-5954 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : multivariate quantile * regression quantile * halfspace depth * depth contour Subject RIV: BD - Theory of Information OBOR OECD: Applied mathematics Impact factor: 0.379, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/bocek-0476587.pdf
Polylinear regression analysis in radiochemistry
International Nuclear Information System (INIS)
Kopyrin, A.A.; Terent'eva, T.N.; Khramov, N.N.
1995-01-01
A number of radiochemical problems have been formulated in the framework of polylinear regression analysis, which permits the use of conventional mathematical methods for their solution. The authors have considered features of the use of polylinear regression analysis for estimating the contributions of various sources to the atmospheric pollution, for studying irradiated nuclear fuel, for estimating concentrations from spectral data, for measuring neutron fields of a nuclear reactor, for estimating crystal lattice parameters from X-ray diffraction patterns, for interpreting data of X-ray fluorescence analysis, for estimating complex formation constants, and for analyzing results of radiometric measurements. The problem of estimating the target parameters can be incorrect at certain properties of the system under study. The authors showed the possibility of regularization by adding a fictitious set of data open-quotes obtainedclose quotes from the orthogonal design. To estimate only a part of the parameters under consideration, the authors used incomplete rank models. In this case, it is necessary to take into account the possibility of confounding estimates. An algorithm for evaluating the degree of confounding is presented which is realized using standard software or regression analysis
Gaussian Process Regression Model in Spatial Logistic Regression
Sofro, A.; Oktaviarina, A.
2018-01-01
Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.
Spontaneous regression of pulmonary bullae
International Nuclear Information System (INIS)
Satoh, H.; Ishikawa, H.; Ohtsuka, M.; Sekizawa, K.
2002-01-01
The natural history of pulmonary bullae is often characterized by gradual, progressive enlargement. Spontaneous regression of bullae is, however, very rare. We report a case in which complete resolution of pulmonary bullae in the left upper lung occurred spontaneously. The management of pulmonary bullae is occasionally made difficult because of gradual progressive enlargement associated with abnormal pulmonary function. Some patients have multiple bulla in both lungs and/or have a history of pulmonary emphysema. Others have a giant bulla without emphysematous change in the lungs. Our present case had treated lung cancer with no evidence of local recurrence. He had no emphysematous change in lung function test and had no complaints, although the high resolution CT scan shows evidence of underlying minimal changes of emphysema. Ortin and Gurney presented three cases of spontaneous reduction in size of bulla. Interestingly, one of them had a marked decrease in the size of a bulla in association with thickening of the wall of the bulla, which was observed in our patient. This case we describe is of interest, not only because of the rarity with which regression of pulmonary bulla has been reported in the literature, but also because of the spontaneous improvements in the radiological picture in the absence of overt infection or tumor. Copyright (2002) Blackwell Science Pty Ltd
Quantum algorithm for linear regression
Wang, Guoming
2017-07-01
We present a quantum algorithm for fitting a linear regression model to a given data set using the least-squares approach. Differently from previous algorithms which yield a quantum state encoding the optimal parameters, our algorithm outputs these numbers in the classical form. So by running it once, one completely determines the fitted model and then can use it to make predictions on new data at little cost. Moreover, our algorithm works in the standard oracle model, and can handle data sets with nonsparse design matrices. It runs in time poly( log2(N ) ,d ,κ ,1 /ɛ ) , where N is the size of the data set, d is the number of adjustable parameters, κ is the condition number of the design matrix, and ɛ is the desired precision in the output. We also show that the polynomial dependence on d and κ is necessary. Thus, our algorithm cannot be significantly improved. Furthermore, we also give a quantum algorithm that estimates the quality of the least-squares fit (without computing its parameters explicitly). This algorithm runs faster than the one for finding this fit, and can be used to check whether the given data set qualifies for linear regression in the first place.
Interpretation of commonly used statistical regression models.
Kasza, Jessica; Wolfe, Rory
2014-01-01
A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.
Prediction, Regression and Critical Realism
DEFF Research Database (Denmark)
Næss, Petter
2004-01-01
This paper considers the possibility of prediction in land use planning, and the use of statistical research methods in analyses of relationships between urban form and travel behaviour. Influential writers within the tradition of critical realism reject the possibility of predicting social...... phenomena. This position is fundamentally problematic to public planning. Without at least some ability to predict the likely consequences of different proposals, the justification for public sector intervention into market mechanisms will be frail. Statistical methods like regression analyses are commonly...... seen as necessary in order to identify aggregate level effects of policy measures, but are questioned by many advocates of critical realist ontology. Using research into the relationship between urban structure and travel as an example, the paper discusses relevant research methods and the kinds...
On Weighted Support Vector Regression
DEFF Research Database (Denmark)
Han, Xixuan; Clemmensen, Line Katrine Harder
2014-01-01
We propose a new type of weighted support vector regression (SVR), motivated by modeling local dependencies in time and space in prediction of house prices. The classic weights of the weighted SVR are added to the slack variables in the objective function (OF‐weights). This procedure directly...... shrinks the coefficient of each observation in the estimated functions; thus, it is widely used for minimizing influence of outliers. We propose to additionally add weights to the slack variables in the constraints (CF‐weights) and call the combination of weights the doubly weighted SVR. We illustrate...... the differences and similarities of the two types of weights by demonstrating the connection between the Least Absolute Shrinkage and Selection Operator (LASSO) and the SVR. We show that an SVR problem can be transformed to a LASSO problem plus a linear constraint and a box constraint. We demonstrate...
Directory of Open Access Journals (Sweden)
Dessie Salilew-Wondim
Full Text Available This study aimed to investigate the miRNA expression patterns in granulosa cells of subordinate (SF and dominant follicle (DF during the early luteal phase of the bovine estrous cycle. For this, miRNA enriched total RNA isolated from granulosa cells of SF and DF obtained from heifers slaughtered at day 3 and day 7 of the estrous cycle was used for miRNAs deep sequencing. The results revealed that including 17 candidate novel miRNAs, several known miRNAs (n = 291-318 were detected in SF and DF at days 3 and 7 of the estrous cycle of which 244 miRNAs were common to all follicle groups. The let-7 families, bta-miR-10b, bta-miR-26a, bta-miR-99b and bta-miR-27b were among abundantly expressed miRNAs in both SF and DF at both days of the estrous cycle. Further analysis revealed that the expression patterns of 16 miRNAs including bta-miR-449a, bta-miR-449c and bta-miR-222 were differentially expressed between the granulosa cells of SF and DF at day 3 of the estrous cycle. However, at day 7 of the estrous cycle, 108 miRNAs including bta-miR-409a, bta-miR-383 and bta-miR-184 were differentially expressed between the two groups of granulosa cell revealing the presence of distinct miRNA expression profile changes between the two follicular stages at day 7 than day 3 of the estrous cycle. In addition, unlike the SF, marked temporal miRNA expression dynamics was observed in DF groups between day 3 and 7 of the estrous cycle. Target gene prediction and pathway analysis revealed that major signaling associated with follicular development including Wnt signaling, TGF-beta signaling, oocyte meiosis and GnRH signaling were affected by differentially expressed miRNAs. Thus, this study highlights the miRNA expression patterns of granulosa cells in subordinate and dominant follicles that could be associated with follicular recruitment, selection and dominance during the early luteal phase of the bovine estrous cycle.
Directory of Open Access Journals (Sweden)
Tjok Gde Oka Pemayun
2012-03-01
Full Text Available The aims of this research were to determine PGF2? concentration the produced by bali cattlesendometrial and seminal vesicle monolayer cell culture and in vitro luteolytic ability on luteal monolayercell culture. The endometrial and seminal vesicle epithelial cell of bali cattle were cultured in tissueculture medium (TCM 199 growth medium supplemented with 10% fetal calf serum and 10% EstrusMare Serum. The cells were cultured at 1.9 x 106 density per ml medium. Then Followed by incubation at38.50 C in 5% CO2 atmosphere for 12 days. The level of PGF2? in the cell culture medium were assayed byRadioimmnuassay (RIA technique. The luteal cells were cultured in 9 days incubation and divided into 2groups. Group I were added with 10% of cell culture product and group II were added with 1,25 mgdinoprost/ml. The level of progesterone produced by luteal cell culture was measured at day 9th and 11thincubation. The result showed concentration of PGF2? cell product of seminal vesicle cell culture wassignificantly higher (P < 0.05 compared to endometrial cell culture. There was no significant difference(P>0.05 in luteolytic ability between PGF2? cell culture product and dinoprost. In conclusion, the PGF2?could be produced by monolayer cell culture of bali cattle is endometrial and seminal vesicle epithelialcells more over they have similar ability with dinoprost in luteolytic ability.
Energy Technology Data Exchange (ETDEWEB)
Kim, Tae Rim; Lee, Hee Min; Lee, So Yong; Kim, Eun Jin; Kim, Kug Chan [Department of Radiation Biology, Environmental Radiation Research Group, Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Paik, Sang Gi [Department of Biology, School of Biosciences and Biotechnology, Chungnam National University, Daejeon (Korea, Republic of); Cho, Eun Wie, E-mail: ewcho@kribb.re.kr [Daejeon-KRIBB-FHCRC Cooperation Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon (Korea, Republic of); Kim, In Gyu, E-mail: igkim@kaeri.re.kr [Department of Radiation Biology, Environmental Radiation Research Group, Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2010-09-10
Research highlights: {yields} SM22{alpha} overexpression in HepG2 cells leads cells to a growth arrest state, and the treatment of a subclinical dose of {gamma}-radiation or doxorubicin promotes cellular senescence. {yields} SM22{alpha} overexpression elevates p16{sup INK4a} followed by pRB activation, but there are no effects on p53/p21{sup WAF1/Cip1} pathway. {yields} SM22{alpha}-induced MT-1G activates p16{sup INK4a}/pRB pathway, which promotes cellular senescence by damaging agents. -- Abstract: Smooth muscle protein 22-alpha (SM22{alpha}) is known as a transformation- and shape change-sensitive actin cross-linking protein found in smooth muscle tissue and fibroblasts; however, its functional role remains uncertain. We reported previously that SM22{alpha} overexpression confers resistance against anti-cancer drugs or radiation via induction of metallothionein (MT) isozymes in HepG2 cells. In this study, we demonstrate that SM22{alpha} overexpression leads cells to a growth arrest state and promotes cellular senescence caused by treatment with a subclinical dose of {gamma}-radiation (0.05 and 0.1 Gy) or doxorubicin (0.01 and 0.05 {mu}g/ml), compared to control cells. Senescence growth arrest is known to be controlled by p53 phosphorylation/p21{sup WAF1/Cip1} induction or p16{sup INK4a}/retinoblastoma protein (pRB) activation. SM22{alpha} overexpression in HepG2 cells elevated p16{sup INK4a} followed by pRB activation, but did not activate the p53/p21{sup WAF1/Cip1} pathway. Moreover, MT-1G, which is induced by SM22{alpha} overexpression, was involved in the activation of the p16{sup INK4a}/pRB pathway, which led to a growth arrest state and promoted cellular senescence caused by damaging agents. Our findings provide the first demonstration that SM22{alpha} modulates cellular senescence caused by damaging agents via regulation of the p16{sup INK4a}/pRB pathway in HepG2 cells and that these effects of SM22{alpha} are partially mediated by MT-1G.
Credit Scoring Problem Based on Regression Analysis
Khassawneh, Bashar Suhil Jad Allah
2014-01-01
ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....
Regularized Label Relaxation Linear Regression.
Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung; Fang, Bingwu
2018-04-01
Linear regression (LR) and some of its variants have been widely used for classification problems. Most of these methods assume that during the learning phase, the training samples can be exactly transformed into a strict binary label matrix, which has too little freedom to fit the labels adequately. To address this problem, in this paper, we propose a novel regularized label relaxation LR method, which has the following notable characteristics. First, the proposed method relaxes the strict binary label matrix into a slack variable matrix by introducing a nonnegative label relaxation matrix into LR, which provides more freedom to fit the labels and simultaneously enlarges the margins between different classes as much as possible. Second, the proposed method constructs the class compactness graph based on manifold learning and uses it as the regularization item to avoid the problem of overfitting. The class compactness graph is used to ensure that the samples sharing the same labels can be kept close after they are transformed. Two different algorithms, which are, respectively, based on -norm and -norm loss functions are devised. These two algorithms have compact closed-form solutions in each iteration so that they are easily implemented. Extensive experiments show that these two algorithms outperform the state-of-the-art algorithms in terms of the classification accuracy and running time.
Shastri, Shefali M; Barbieri, Elizabeth; Kligman, Isaac; Schoyer, Katherine D; Davis, Owen K; Rosenwaks, Zev
2011-02-01
To evaluate in vitro fertilization (IVF) cycle outcomes in young poor responders treated with a luteal estradiol/gonadotropin-releasing hormone antagonist (E(2)/ANT) protocol versus an oral contraceptive pill microdose leuprolide protocol (OCP-MDL). Retrospective cohort. Academic practice. Poor responders: 186 women, aged <35 years undergoing IVF with either E(2)/ANT or OCP-MDL protocols. None. Clinical pregnancies, oocytes retrieved, cancellation rate. Patients in the E(2)/ANT group had a greater gonadotropin requirement (71.9 ± 22.2 vs. 57.6 ± 25.7) and lower E(2) level (1,178.6 ± 668 vs. 1,627 ± 889), yet achieved similar numbers of oocytes retrieved and fertilized, and a greater number of embryos transferred (2.3 ± 0.9 vs. 2.0 ± 1.1) with a better mean grade (2.14 ± .06 vs. 2.7 ± 1.8) compared with the OCP/MDL group. The E2/ANT group exhibited a trend toward improved implantation rates (30.5% vs. 21.1%) and ongoing pregnancy rates per started cycle: 44 out of 117 (37%) versus 17 out of 69 (25%). Poor responders aged <35 years may be treated with the aggressive E(2)/ANT protocol to improve cycle outcomes. Both protocols remain viable options for this group. Adequately powered, randomized clinical comparison appears justified. Copyright © 2011 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.
Principal component regression analysis with SPSS.
Liu, R X; Kuang, J; Gong, Q; Hou, X L
2003-06-01
The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.
Unbalanced Regressions and the Predictive Equation
DEFF Research Database (Denmark)
Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo
Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...
Semiparametric regression during 2003–2007
Ruppert, David; Wand, M.P.; Carroll, Raymond J.
2009-01-01
Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.
Gaussian process regression analysis for functional data
Shi, Jian Qing
2011-01-01
Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime
Regression Analysis by Example. 5th Edition
Chatterjee, Samprit; Hadi, Ali S.
2012-01-01
Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…
Standards for Standardized Logistic Regression Coefficients
Menard, Scott
2011-01-01
Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…
A Seemingly Unrelated Poisson Regression Model
King, Gary
1989-01-01
This article introduces a new estimator for the analysis of two contemporaneously correlated endogenous event count variables. This seemingly unrelated Poisson regression model (SUPREME) estimator combines the efficiencies created by single equation Poisson regression model estimators and insights from "seemingly unrelated" linear regression models.
Regression with Sparse Approximations of Data
DEFF Research Database (Denmark)
Noorzad, Pardis; Sturm, Bob L.
2012-01-01
We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...
Spontaneous regression of a congenital melanocytic nevus
Directory of Open Access Journals (Sweden)
Amiya Kumar Nath
2011-01-01
Full Text Available Congenital melanocytic nevus (CMN may rarely regress which may also be associated with a halo or vitiligo. We describe a 10-year-old girl who presented with CMN on the left leg since birth, which recently started to regress spontaneously with associated depigmentation in the lesion and at a distant site. Dermoscopy performed at different sites of the regressing lesion demonstrated loss of epidermal pigments first followed by loss of dermal pigments. Histopathology and Masson-Fontana stain demonstrated lymphocytic infiltration and loss of pigment production in the regressing area. Immunohistochemistry staining (S100 and HMB-45, however, showed that nevus cells were present in the regressing areas.
Manikkam, Mohan; Steckler, Teresa L; Welch, Kathleen B; Inskeep, E Keith; Padmanabhan, Vasantha
2006-04-01
Prenatal testosterone (T) excess during midgestation leads to estrous cycle defects and polycystic ovaries in sheep. We hypothesized that follicular persistence causes polycystic ovaries and that cyclic progesterone (P) treatment would overcome follicular persistence and restore cyclicity. Twice-weekly blood samples for P measurements were taken from control (C; n = 16) and prenatally T-treated (T60; n = 14; 100 mg T, im, twice weekly from d 30-90 of gestation) Suffolk sheep starting before the onset of puberty and continuing through the second breeding season. A subset of C and T60 sheep were treated cyclically with a modified controlled internal drug-releasing device for 13-14 d every 17 d during the first anestrus (CP, 7; TP, 6). Transrectal ovarian ultrasonography was performed for 8 d in the first and 21 d in the second breeding season. Prenatal T excess reduced the number, but increased the duration of progestogenic cycles, reduced the proportion of ewes with normal cycles, increased the proportion of ewes with subluteal cycles, decreased the proportion of ewes with ovulatory cycles, induced the occurrence of persistent follicles, and reduced the number of corpora lutea in those that cycled. Cyclic P treatment in anestrus, which produced one third the P concentration seen during luteal phase of cycle, did not reduce the number of persistent follicles, but increased the number of progestogenic cycles while reducing their duration. These findings suggested that follicular persistence might contribute to the polycystic ovarian morphology. Cyclic P treatment was able to only partially restore follicular dynamics, but this may be related to the low replacement concentrations of P achieved.
Ciechanowska, Magdalena; Lapot, Magdalena; Malewski, Tadeusz; Mateusiak, Krystyna; Misztal, Tomasz; Przekop, Franciszek
2008-11-01
Data exists showing that seasonal changes in the innervations of GnRH cells in the hypothalamus and functions of some neural systems affecting GnRH neurons are associated with GnRH release in ewes. Consequently, we put the question as to how the expression of GnRH gene and GnRH-R gene in the hypothalamus and GnRH-R gene in the anterior pituitary gland is reflected with LH secretion in anestrous and luteal phase ewes. Analysis of GnRH gene expression by RT-PCR in anestrous ewes indicated comparable levels of GnRH mRNA in the preoptic area, anterior and ventromedial hypothalamus. GnRH-R mRNA at different concentrations was found throughout the preoptic area, anterior and ventromedial hypothalamus, stalk/median eminence and in the anterior pituitary gland. The highest GnRH-R mRNA levels were detected in the stalk/median eminence and in the anterior pituitary gland. During the luteal phase of the estrous cycle in ewes, the levels of GnRH mRNA and GnRH-R mRNA in all structures were significantly higher than in anestrous ewes. Also LH concentrations in blood plasma of luteal phase ewes were significantly higher than those of anestrous ewes. In conclusion, results from this study suggest that low expression of the GnRH and GnRH-R genes in the hypothalamus and of the GnRH-R gene in the anterior pituitary gland, amongst others, may be responsible for a decrease in LH secretion and the anovulatory state in ewes during the long photoperiod.
Applied regression analysis a research tool
Pantula, Sastry; Dickey, David
1998-01-01
Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...
Regression models of reactor diagnostic signals
International Nuclear Information System (INIS)
Vavrin, J.
1989-01-01
The application is described of an autoregression model as the simplest regression model of diagnostic signals in experimental analysis of diagnostic systems, in in-service monitoring of normal and anomalous conditions and their diagnostics. The method of diagnostics is described using a regression type diagnostic data base and regression spectral diagnostics. The diagnostics is described of neutron noise signals from anomalous modes in the experimental fuel assembly of a reactor. (author)
Directory of Open Access Journals (Sweden)
Evangelos G. Papanikolaou
2018-03-01
Full Text Available IntroductionA drawback of gonadotropin-releasing hormone (GnRH antagonist protocols in in vitro fertilization (IVF is that they have limited flexibility in cycle programming. This proof of concept study explored the efficacy of a single-dose, long-acting GnRH antagonist IVF protocol. Trial registration number is NCT03240159, retrospectively registered on March 08, 2017.Materials and methodsThe efficacy of a single-dose long-acting antagonist, degarelix, was explored initially in healthy donors and subsequently in infertile patients. In the first part, five healthy oocyte donors underwent ovarian stimulation with this new protocol: in the late luteal phase, at day 24, a bolus injection of degarelix was administered subcutaneously to control the LH surge in the follicular phase. Ovarian stimulation with gonadotropins was initiated subsequently from day 7 to day 10. End points were first to inhibit the LH surge later in the follicular phase and, second, to retrieve mature oocytes for IVF. In the second part, five infertile women received the same bolus injection of degarelix administered during the luteal phase at day 24. Different gonadotropin starting days (day 2 through day 8 were tested in order to observe possible differences in ovarian stimulation. In these infertile patients, fresh embryo transfers were performed to assess the pregnancy efficacy of this protocol on pregnancy outcomes and to address any possible negative effects on endometrium receptivity.ResultsIn the first part of the study, all donors were effectively downregulated with a single luteal dose of 0.5 ml of degarelix for up to 22 days until the final oocyte maturation triggering day. Mature oocytes were retrieved after 36 h from all patients and all produced 2–7 blastocysts. In the second part, all five infertile patients achieved sufficient LH downregulation and completed ovarian stimulation without any LH surge. All patients (except one with freeze all strategy had
International Nuclear Information System (INIS)
Vopelius-Feldt, F. von.
1986-01-01
The relationship between carcinomas of the prostate and the plasma levels of testosterone, luteal hormone and prolactin as well as the possible influence of these neoplasms on the testosterone binding capacity and free testosterone index are investigated for various tumour stages and degrees of histological differentiation, in connection with several forms of local therapy as well as a variety of contrasexual methods. The sensitivity of enzyme assays and radioimmunoassays for the detection of acid prostate phosphatase is evaluated within the framework of this study. (MBL) [de
Bulcock, J. W.
The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…
Multivariate Regression Analysis and Slaughter Livestock,
AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY
[From clinical judgment to linear regression model.
Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2013-01-01
When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.
Regression modeling methods, theory, and computation with SAS
Panik, Michael
2009-01-01
Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,
RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,
This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)
A Simulation Investigation of Principal Component Regression.
Allen, David E.
Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…
Hierarchical regression analysis in structural Equation Modeling
de Jong, P.F.
1999-01-01
In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main
Categorical regression dose-response modeling
The goal of this training is to provide participants with training on the use of the U.S. EPA’s Categorical Regression soft¬ware (CatReg) and its application to risk assessment. Categorical regression fits mathematical models to toxicity data that have been assigned ord...
Variable importance in latent variable regression models
Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.
2014-01-01
The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable
Stepwise versus Hierarchical Regression: Pros and Cons
Lewis, Mitzi
2007-01-01
Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…
Suppression Situations in Multiple Linear Regression
Shieh, Gwowen
2006-01-01
This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…
Gibrat’s law and quantile regressions
DEFF Research Database (Denmark)
Distante, Roberta; Petrella, Ivan; Santoro, Emiliano
2017-01-01
The nexus between firm growth, size and age in U.S. manufacturing is examined through the lens of quantile regression models. This methodology allows us to overcome serious shortcomings entailed by linear regression models employed by much of the existing literature, unveiling a number of important...
Regression Analysis and the Sociological Imagination
De Maio, Fernando
2014-01-01
Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.
Repeated Results Analysis for Middleware Regression Benchmarking
Czech Academy of Sciences Publication Activity Database
Bulej, Lubomír; Kalibera, T.; Tůma, P.
2005-01-01
Roč. 60, - (2005), s. 345-358 ISSN 0166-5316 R&D Projects: GA ČR GA102/03/0672 Institutional research plan: CEZ:AV0Z10300504 Keywords : middleware benchmarking * regression benchmarking * regression testing Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.756, year: 2005
Principles of Quantile Regression and an Application
Chen, Fang; Chalhoub-Deville, Micheline
2014-01-01
Newer statistical procedures are typically introduced to help address the limitations of those already in practice or to deal with emerging research needs. Quantile regression (QR) is introduced in this paper as a relatively new methodology, which is intended to overcome some of the limitations of least squares mean regression (LMR). QR is more…
ON REGRESSION REPRESENTATIONS OF STOCHASTIC-PROCESSES
RUSCHENDORF, L; DEVALK, [No Value
We construct a.s. nonlinear regression representations of general stochastic processes (X(n))n is-an-element-of N. As a consequence we obtain in particular special regression representations of Markov chains and of certain m-dependent sequences. For m-dependent sequences we obtain a constructive
Regression of environmental noise in LIGO data
International Nuclear Information System (INIS)
Tiwari, V; Klimenko, S; Mitselmakher, G; Necula, V; Drago, M; Prodi, G; Frolov, V; Yakushin, I; Re, V; Salemi, F; Vedovato, G
2015-01-01
We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the GW channel from the PEM measurements. One of the most promising regression methods is based on the construction of Wiener–Kolmogorov (WK) filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the WK method has been extended, incorporating banks of Wiener filters in the time–frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we present the first results on regression of the bi-coherent noise in the LIGO data. (paper)
Pathological assessment of liver fibrosis regression
Directory of Open Access Journals (Sweden)
WANG Bingqiong
2017-03-01
Full Text Available Hepatic fibrosis is the common pathological outcome of chronic hepatic diseases. An accurate assessment of fibrosis degree provides an important reference for a definite diagnosis of diseases, treatment decision-making, treatment outcome monitoring, and prognostic evaluation. At present, many clinical studies have proven that regression of hepatic fibrosis and early-stage liver cirrhosis can be achieved by effective treatment, and a correct evaluation of fibrosis regression has become a hot topic in clinical research. Liver biopsy has long been regarded as the gold standard for the assessment of hepatic fibrosis, and thus it plays an important role in the evaluation of fibrosis regression. This article reviews the clinical application of current pathological staging systems in the evaluation of fibrosis regression from the perspectives of semi-quantitative scoring system, quantitative approach, and qualitative approach, in order to propose a better pathological evaluation system for the assessment of fibrosis regression.
Should metacognition be measured by logistic regression?
Rausch, Manuel; Zehetleitner, Michael
2017-03-01
Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
I Pokora
2003-12-01
Full Text Available The aim of this study was to examine the effects of a low-carbohydrate diet on thermoregulatory responses to exercise in women during follicular (F and luteal (L phase of the menstrual cycle. Ten subjects performed a graded bicycle exercise in a thermoneutral environment (23oC, 52-60% relative humidity. Women were tested after consuming, for 3 days, a control diet (C: 60% carbohydrates, 20% fat, 20% protein and after that a low-carbohydrate diet (LCHO: 50% fat, 35% protein and 5% carbohydrates, in each phase of the menstrual cycle. Tympanic temperature (Tty, mean skin temperature (Tsk, electrical skin resistance (ESR, oxygen uptake (VO2, heart rate (HR as well as blood β-hydroxybutyrate acid (β-HB, glucose (Glu and lactate (LA concentrations were measured. On the basis of ESR, dynamics of sweating was estimated. No differences in Tty and Tsk were found between the C and LCHO during exercise tests. However, Tty was significantly higher during L than F phase. Delay time for sweating was shorter after LCHO (F: 10.8 vs 9.4 min, P<0.05, L: 9.9 vs 9.3 N.S., but temperature threshold for this reaction was unchanged (L: 37.22 vs 37.37 and F: 36.91 vs 36.94 oC. Sweating sensitivity was greater after LCHO during both F and L. Resting blood Glu and LA concentrations were similar in women after C and LCHO diet. Before exercise β-HB level was F: 0.45, L: 0.35 mM after LCHO and F: 0.08, L: 0.09 mM after C diet (P<0.05, respectively. At rest and during exercise HR was significantly higher after LCHO diet in women during F phase. In submaximal exercise loads VO2 after LCHO diet were significantly higher than after C diet in all women. It was concluded that the low-carbohydrate diet ingested by young women in both phases of the menstrual cycle have no effect on body temperature, however, it affects heat dissipation mechanism during exercise.
Hayashi, Ken-Go; Hosoe, Misa; Kizaki, Keiichiro; Fujii, Shiori; Kanahara, Hiroko; Takahashi, Toru; Sakumoto, Ryosuke
2017-03-23
Repeat breeding directly affects reproductive efficiency in cattle due to an increase in services per conception and calving interval. This study aimed to investigate whether changes in endometrial gene expression profile are involved in repeat breeding in cows. Differential gene expression profiles of the endometrium were investigated during the mid-luteal phase of the estrous cycle between repeat breeder (RB) and non-RB cows using microarray analysis. The caruncular (CAR) and intercaruncular (ICAR) endometrium of both ipsilateral and contralateral uterine horns to the corpus luteum were collected from RB (inseminated at least three times but not pregnant) and non-RB cows on Day 15 of the estrous cycle (4 cows/group). Global gene expression profiles of these endometrial samples were analyzed with a 15 K custom-made oligo-microarray for cattle. Immunohistochemistry was performed to investigate the cellular localization of proteins of three identified transcripts in the endometrium. Microarray analysis revealed that 405 and 397 genes were differentially expressed in the CAR and ICAR of the ipsilateral uterine horn of RB, respectively when compared with non-RB cows. In the contralateral uterine horn, 443 and 257 differentially expressed genes were identified in the CAR and ICAR of RB, respectively when compared with non-RB cows. Gene ontology analysis revealed that genes involved in development and morphogenesis were mainly up-regulated in the CAR of RB cows. In the ICAR of both the ipsilateral and contralateral uterine horns, genes related to the metabolic process were predominantly enriched in the RB cows when compared with non-RB cows. In the analysis of the whole uterus (combining the data above four endometrial compartments), RB cows showed up-regulation of 37 genes including PRSS2, GSTA3 and PIPOX and down-regulation of 39 genes including CHGA, KRT35 and THBS4 when compared with non-RB cows. Immunohistochemistry revealed that CHGA, GSTA3 and PRSS2 proteins
Regression modeling of ground-water flow
Cooley, R.L.; Naff, R.L.
1985-01-01
Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)
Variable and subset selection in PLS regression
DEFF Research Database (Denmark)
Høskuldsson, Agnar
2001-01-01
The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion...... is that variable selection is important for successful analysis of chemometric data. An important aspect of the results presented is that lack of variable selection can spoil the PLS regression, and that cross-validation measures using a test set can show larger variation, when we use different subsets of X, than...
Applied Regression Modeling A Business Approach
Pardoe, Iain
2012-01-01
An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a
Vectors, a tool in statistical regression theory
Corsten, L.C.A.
1958-01-01
Using linear algebra this thesis developed linear regression analysis including analysis of variance, covariance analysis, special experimental designs, linear and fertility adjustments, analysis of experiments at different places and times. The determination of the orthogonal projection, yielding
Genetics Home Reference: caudal regression syndrome
... umbilical artery: Further support for a caudal regression-sirenomelia spectrum. Am J Med Genet A. 2007 Dec ... AK, Dickinson JE, Bower C. Caudal dysgenesis and sirenomelia-single centre experience suggests common pathogenic basis. Am ...
Dynamic travel time estimation using regression trees.
2008-10-01
This report presents a methodology for travel time estimation by using regression trees. The dissemination of travel time information has become crucial for effective traffic management, especially under congested road conditions. In the absence of c...
Two Paradoxes in Linear Regression Analysis
FENG, Ge; PENG, Jing; TU, Dongke; ZHENG, Julia Z.; FENG, Changyong
2016-01-01
Summary Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection. PMID:28638214
Discriminative Elastic-Net Regularized Linear Regression.
Zhang, Zheng; Lai, Zhihui; Xu, Yong; Shao, Ling; Wu, Jian; Xie, Guo-Sen
2017-03-01
In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zero-one matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of these methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available data sets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html.
Fuzzy multiple linear regression: A computational approach
Juang, C. H.; Huang, X. H.; Fleming, J. W.
1992-01-01
This paper presents a new computational approach for performing fuzzy regression. In contrast to Bardossy's approach, the new approach, while dealing with fuzzy variables, closely follows the conventional regression technique. In this approach, treatment of fuzzy input is more 'computational' than 'symbolic.' The following sections first outline the formulation of the new approach, then deal with the implementation and computational scheme, and this is followed by examples to illustrate the new procedure.
Computing multiple-output regression quantile regions
Czech Academy of Sciences Publication Activity Database
Paindaveine, D.; Šiman, Miroslav
2012-01-01
Roč. 56, č. 4 (2012), s. 840-853 ISSN 0167-9473 R&D Projects: GA MŠk(CZ) 1M06047 Institutional research plan: CEZ:AV0Z10750506 Keywords : halfspace depth * multiple-output regression * parametric linear programming * quantile regression Subject RIV: BA - General Mathematics Impact factor: 1.304, year: 2012 http://library.utia.cas.cz/separaty/2012/SI/siman-0376413.pdf
There is No Quantum Regression Theorem
International Nuclear Information System (INIS)
Ford, G.W.; OConnell, R.F.
1996-01-01
The Onsager regression hypothesis states that the regression of fluctuations is governed by macroscopic equations describing the approach to equilibrium. It is here asserted that this hypothesis fails in the quantum case. This is shown first by explicit calculation for the example of quantum Brownian motion of an oscillator and then in general from the fluctuation-dissipation theorem. It is asserted that the correct generalization of the Onsager hypothesis is the fluctuation-dissipation theorem. copyright 1996 The American Physical Society
Caudal regression syndrome : a case report
International Nuclear Information System (INIS)
Lee, Eun Joo; Kim, Hi Hye; Kim, Hyung Sik; Park, So Young; Han, Hye Young; Lee, Kwang Hun
1998-01-01
Caudal regression syndrome is a rare congenital anomaly, which results from a developmental failure of the caudal mesoderm during the fetal period. We present a case of caudal regression syndrome composed of a spectrum of anomalies including sirenomelia, dysplasia of the lower lumbar vertebrae, sacrum, coccyx and pelvic bones,genitourinary and anorectal anomalies, and dysplasia of the lung, as seen during infantography and MR imaging
Caudal regression syndrome : a case report
Energy Technology Data Exchange (ETDEWEB)
Lee, Eun Joo; Kim, Hi Hye; Kim, Hyung Sik; Park, So Young; Han, Hye Young; Lee, Kwang Hun [Chungang Gil Hospital, Incheon (Korea, Republic of)
1998-07-01
Caudal regression syndrome is a rare congenital anomaly, which results from a developmental failure of the caudal mesoderm during the fetal period. We present a case of caudal regression syndrome composed of a spectrum of anomalies including sirenomelia, dysplasia of the lower lumbar vertebrae, sacrum, coccyx and pelvic bones,genitourinary and anorectal anomalies, and dysplasia of the lung, as seen during infantography and MR imaging.
Spontaneous regression of metastatic Merkel cell carcinoma.
LENUS (Irish Health Repository)
Hassan, S J
2010-01-01
Merkel cell carcinoma is a rare aggressive neuroendocrine carcinoma of the skin predominantly affecting elderly Caucasians. It has a high rate of local recurrence and regional lymph node metastases. It is associated with a poor prognosis. Complete spontaneous regression of Merkel cell carcinoma has been reported but is a poorly understood phenomenon. Here we present a case of complete spontaneous regression of metastatic Merkel cell carcinoma demonstrating a markedly different pattern of events from those previously published.
Forecasting exchange rates: a robust regression approach
Preminger, Arie; Franck, Raphael
2005-01-01
The least squares estimation method as well as other ordinary estimation method for regression models can be severely affected by a small number of outliers, thus providing poor out-of-sample forecasts. This paper suggests a robust regression approach, based on the S-estimation method, to construct forecasting models that are less sensitive to data contamination by outliers. A robust linear autoregressive (RAR) and a robust neural network (RNN) models are estimated to study the predictabil...
Marginal longitudinal semiparametric regression via penalized splines
Al Kadiri, M.
2010-08-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
Marginal longitudinal semiparametric regression via penalized splines
Al Kadiri, M.; Carroll, R.J.; Wand, M.P.
2010-01-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
Post-processing through linear regression
van Schaeybroeck, B.; Vannitsem, S.
2011-03-01
Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
Post-processing through linear regression
Directory of Open Access Journals (Sweden)
B. Van Schaeybroeck
2011-03-01
Full Text Available Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS method, a new time-dependent Tikhonov regularization (TDTR method, the total least-square method, a new geometric-mean regression (GM, a recently introduced error-in-variables (EVMOS method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified.
These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise. At long lead times the regression schemes (EVMOS, TDTR which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
Unbalanced Regressions and the Predictive Equation
DEFF Research Database (Denmark)
Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo
Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...... in the theoretical predictive equation by suggesting a data generating process, where returns are generated as linear functions of a lagged latent I(0) risk process. The observed predictor is a function of this latent I(0) process, but it is corrupted by a fractionally integrated noise. Such a process may arise due...... to aggregation or unexpected level shifts. In this setup, the practitioner estimates a misspecified, unbalanced, and endogenous predictive regression. We show that the OLS estimate of this regression is inconsistent, but standard inference is possible. To obtain a consistent slope estimate, we then suggest...
N.S. Macklon (Nick); M.J.C. Eijkemans (René); M. Ludwig (Michael); R.E. Felberbaum; K. Diedrich; S. Bustion; E. Loumaye; B.C.J.M. Fauser (Bart); N.G.M. Beckers (Nicole)
2003-01-01
textabstractReplacing GnRH agonist cotreatment for the prevention of a premature rise in LH during ovarian stimulation for in vitro fertilization (IVF) by the late follicular phase administration of GnRH antagonist may render supplementation of the luteal phase redundant, because
Regression analysis using dependent Polya trees.
Schörgendorfer, Angela; Branscum, Adam J
2013-11-30
Many commonly used models for linear regression analysis force overly simplistic shape and scale constraints on the residual structure of data. We propose a semiparametric Bayesian model for regression analysis that produces data-driven inference by using a new type of dependent Polya tree prior to model arbitrary residual distributions that are allowed to evolve across increasing levels of an ordinal covariate (e.g., time, in repeated measurement studies). By modeling residual distributions at consecutive covariate levels or time points using separate, but dependent Polya tree priors, distributional information is pooled while allowing for broad pliability to accommodate many types of changing residual distributions. We can use the proposed dependent residual structure in a wide range of regression settings, including fixed-effects and mixed-effects linear and nonlinear models for cross-sectional, prospective, and repeated measurement data. A simulation study illustrates the flexibility of our novel semiparametric regression model to accurately capture evolving residual distributions. In an application to immune development data on immunoglobulin G antibodies in children, our new model outperforms several contemporary semiparametric regression models based on a predictive model selection criterion. Copyright © 2013 John Wiley & Sons, Ltd.
Is past life regression therapy ethical?
Andrade, Gabriel
2017-01-01
Past life regression therapy is used by some physicians in cases with some mental diseases. Anxiety disorders, mood disorders, and gender dysphoria have all been treated using life regression therapy by some doctors on the assumption that they reflect problems in past lives. Although it is not supported by psychiatric associations, few medical associations have actually condemned it as unethical. In this article, I argue that past life regression therapy is unethical for two basic reasons. First, it is not evidence-based. Past life regression is based on the reincarnation hypothesis, but this hypothesis is not supported by evidence, and in fact, it faces some insurmountable conceptual problems. If patients are not fully informed about these problems, they cannot provide an informed consent, and hence, the principle of autonomy is violated. Second, past life regression therapy has the great risk of implanting false memories in patients, and thus, causing significant harm. This is a violation of the principle of non-malfeasance, which is surely the most important principle in medical ethics.
On Solving Lq-Penalized Regressions
Directory of Open Access Journals (Sweden)
Tracy Zhou Wu
2007-01-01
Full Text Available Lq-penalized regression arises in multidimensional statistical modelling where all or part of the regression coefficients are penalized to achieve both accuracy and parsimony of statistical models. There is often substantial computational difficulty except for the quadratic penalty case. The difficulty is partly due to the nonsmoothness of the objective function inherited from the use of the absolute value. We propose a new solution method for the general Lq-penalized regression problem based on space transformation and thus efficient optimization algorithms. The new method has immediate applications in statistics, notably in penalized spline smoothing problems. In particular, the LASSO problem is shown to be polynomial time solvable. Numerical studies show promise of our approach.
Refractive regression after laser in situ keratomileusis.
Yan, Mabel K; Chang, John Sm; Chan, Tommy Cy
2018-04-26
Uncorrected refractive errors are a leading cause of visual impairment across the world. In today's society, laser in situ keratomileusis (LASIK) has become the most commonly performed surgical procedure to correct refractive errors. However, regression of the initially achieved refractive correction has been a widely observed phenomenon following LASIK since its inception more than two decades ago. Despite technological advances in laser refractive surgery and various proposed management strategies, post-LASIK regression is still frequently observed and has significant implications for the long-term visual performance and quality of life of patients. This review explores the mechanism of refractive regression after both myopic and hyperopic LASIK, predisposing risk factors and its clinical course. In addition, current preventative strategies and therapies are also reviewed. © 2018 Royal Australian and New Zealand College of Ophthalmologists.
Influence diagnostics in meta-regression model.
Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua
2017-09-01
This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.
Principal component regression for crop yield estimation
Suryanarayana, T M V
2016-01-01
This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using climatological parameters as independent variables and crop yield as a dependent variable. It subsequently compares the multiple linear regression (MLR) and PCR results, and discusses the significance of PCR for crop yield estimation. In this context, the book also covers Principal Component Analysis (PCA), a statistical procedure used to reduce a number of correlated variables into a smaller number of uncorrelated variables called principal components (PC). This book will be helpful to the students and researchers, starting their works on climate and agriculture, mainly focussing on estimation models. The flow of chapters takes the readers in a smooth path, in understanding climate and weather and impact of climate change, and gradually proceeds towards downscaling techniques and then finally towards development of ...
Regression Models for Market-Shares
DEFF Research Database (Denmark)
Birch, Kristina; Olsen, Jørgen Kai; Tjur, Tue
2005-01-01
On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put on the interpretat......On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put...... on the interpretation of the parameters in relation to models for the total sales based on discrete choice models.Key words and phrases. MCI model, discrete choice model, market-shares, price elasitcity, regression model....
On directional multiple-output quantile regression
Czech Academy of Sciences Publication Activity Database
Paindaveine, D.; Šiman, Miroslav
2011-01-01
Roč. 102, č. 2 (2011), s. 193-212 ISSN 0047-259X R&D Projects: GA MŠk(CZ) 1M06047 Grant - others:Commision EC(BE) Fonds National de la Recherche Scientifique Institutional research plan: CEZ:AV0Z10750506 Keywords : multivariate quantile * quantile regression * multiple-output regression * halfspace depth * portfolio optimization * value-at risk Subject RIV: BA - General Mathematics Impact factor: 0.879, year: 2011 http://library.utia.cas.cz/separaty/2011/SI/siman-0364128.pdf
Removing Malmquist bias from linear regressions
Verter, Frances
1993-01-01
Malmquist bias is present in all astronomical surveys where sources are observed above an apparent brightness threshold. Those sources which can be detected at progressively larger distances are progressively more limited to the intrinsically luminous portion of the true distribution. This bias does not distort any of the measurements, but distorts the sample composition. We have developed the first treatment to correct for Malmquist bias in linear regressions of astronomical data. A demonstration of the corrected linear regression that is computed in four steps is presented.
Robust median estimator in logisitc regression
Czech Academy of Sciences Publication Activity Database
Hobza, T.; Pardo, L.; Vajda, Igor
2008-01-01
Roč. 138, č. 12 (2008), s. 3822-3840 ISSN 0378-3758 R&D Projects: GA MŠk 1M0572 Grant - others:Instituto Nacional de Estadistica (ES) MPO FI - IM3/136; GA MŠk(CZ) MTM 2006-06872 Institutional research plan: CEZ:AV0Z10750506 Keywords : Logistic regression * Median * Robustness * Consistency and asymptotic normality * Morgenthaler * Bianco and Yohai * Croux and Hasellbroeck Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.679, year: 2008 http://library.utia.cas.cz/separaty/2008/SI/vajda-robust%20median%20estimator%20in%20logistic%20regression.pdf
Demonstration of a Fiber Optic Regression Probe
Korman, Valentin; Polzin, Kurt A.
2010-01-01
The capability to provide localized, real-time monitoring of material regression rates in various applications has the potential to provide a new stream of data for development testing of various components and systems, as well as serving as a monitoring tool in flight applications. These applications include, but are not limited to, the regression of a combusting solid fuel surface, the ablation of the throat in a chemical rocket or the heat shield of an aeroshell, and the monitoring of erosion in long-life plasma thrusters. The rate of regression in the first application is very fast, while the second and third are increasingly slower. A recent fundamental sensor development effort has led to a novel regression, erosion, and ablation sensor technology (REAST). The REAST sensor allows for measurement of real-time surface erosion rates at a discrete surface location. The sensor is optical, using two different, co-located fiber-optics to perform the regression measurement. The disparate optical transmission properties of the two fiber-optics makes it possible to measure the regression rate by monitoring the relative light attenuation through the fibers. As the fibers regress along with the parent material in which they are embedded, the relative light intensities through the two fibers changes, providing a measure of the regression rate. The optical nature of the system makes it relatively easy to use in a variety of harsh, high temperature environments, and it is also unaffected by the presence of electric and magnetic fields. In addition, the sensor could be used to perform optical spectroscopy on the light emitted by a process and collected by fibers, giving localized measurements of various properties. The capability to perform an in-situ measurement of material regression rates is useful in addressing a variety of physical issues in various applications. An in-situ measurement allows for real-time data regarding the erosion rates, providing a quick method for
KELEŞ, Taliha; ALTUN, Murat
2016-01-01
Regression analysis is a statistical technique for investigating and modeling the relationship between variables. The purpose of this study was the trivial presentation of the equation for orthogonal regression (OR) and the comparison of classical linear regression (CLR) and OR techniques with respect to the sum of squared perpendicular distances. For that purpose, the analyses were shown by an example. It was found that the sum of squared perpendicular distances of OR is smaller. Thus, it wa...
Method for nonlinear exponential regression analysis
Junkin, B. G.
1972-01-01
Two computer programs developed according to two general types of exponential models for conducting nonlinear exponential regression analysis are described. Least squares procedure is used in which the nonlinear problem is linearized by expanding in a Taylor series. Program is written in FORTRAN 5 for the Univac 1108 computer.
Measurement Error in Education and Growth Regressions
Portela, Miguel; Alessie, Rob; Teulings, Coen
2010-01-01
The use of the perpetual inventory method for the construction of education data per country leads to systematic measurement error. This paper analyzes its effect on growth regressions. We suggest a methodology for correcting this error. The standard attenuation bias suggests that using these
The M Word: Multicollinearity in Multiple Regression.
Morrow-Howell, Nancy
1994-01-01
Notes that existence of substantial correlation between two or more independent variables creates problems of multicollinearity in multiple regression. Discusses multicollinearity problem in social work research in which independent variables are usually intercorrelated. Clarifies problems created by multicollinearity, explains detection of…
Regression Discontinuity Designs Based on Population Thresholds
DEFF Research Database (Denmark)
Eggers, Andrew C.; Freier, Ronny; Grembi, Veronica
In many countries, important features of municipal government (such as the electoral system, mayors' salaries, and the number of councillors) depend on whether the municipality is above or below arbitrary population thresholds. Several papers have used a regression discontinuity design (RDD...
Deriving the Regression Line with Algebra
Quintanilla, John A.
2017-01-01
Exploration with spreadsheets and reliance on previous skills can lead students to determine the line of best fit. To perform linear regression on a set of data, students in Algebra 2 (or, in principle, Algebra 1) do not have to settle for using the mysterious "black box" of their graphing calculators (or other classroom technologies).…
Piecewise linear regression splines with hyperbolic covariates
International Nuclear Information System (INIS)
Cologne, John B.; Sposto, Richard
1992-09-01
Consider the problem of fitting a curve to data that exhibit a multiphase linear response with smooth transitions between phases. We propose substituting hyperbolas as covariates in piecewise linear regression splines to obtain curves that are smoothly joined. The method provides an intuitive and easy way to extend the two-phase linear hyperbolic response model of Griffiths and Miller and Watts and Bacon to accommodate more than two linear segments. The resulting regression spline with hyperbolic covariates may be fit by nonlinear regression methods to estimate the degree of curvature between adjoining linear segments. The added complexity of fitting nonlinear, as opposed to linear, regression models is not great. The extra effort is particularly worthwhile when investigators are unwilling to assume that the slope of the response changes abruptly at the join points. We can also estimate the join points (the values of the abscissas where the linear segments would intersect if extrapolated) if their number and approximate locations may be presumed known. An example using data on changing age at menarche in a cohort of Japanese women illustrates the use of the method for exploratory data analysis. (author)
Targeting: Logistic Regression, Special Cases and Extensions
Directory of Open Access Journals (Sweden)
Helmut Schaeben
2014-12-01
Full Text Available Logistic regression is a classical linear model for logit-transformed conditional probabilities of a binary target variable. It recovers the true conditional probabilities if the joint distribution of predictors and the target is of log-linear form. Weights-of-evidence is an ordinary logistic regression with parameters equal to the differences of the weights of evidence if all predictor variables are discrete and conditionally independent given the target variable. The hypothesis of conditional independence can be tested in terms of log-linear models. If the assumption of conditional independence is violated, the application of weights-of-evidence does not only corrupt the predicted conditional probabilities, but also their rank transform. Logistic regression models, including the interaction terms, can account for the lack of conditional independence, appropriate interaction terms compensate exactly for violations of conditional independence. Multilayer artificial neural nets may be seen as nested regression-like models, with some sigmoidal activation function. Most often, the logistic function is used as the activation function. If the net topology, i.e., its control, is sufficiently versatile to mimic interaction terms, artificial neural nets are able to account for violations of conditional independence and yield very similar results. Weights-of-evidence cannot reasonably include interaction terms; subsequent modifications of the weights, as often suggested, cannot emulate the effect of interaction terms.
Functional data analysis of generalized regression quantiles
Guo, Mengmeng
2013-11-05
Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.
Regression testing Ajax applications : Coping with dynamism
Roest, D.; Mesbah, A.; Van Deursen, A.
2009-01-01
Note: This paper is a pre-print of: Danny Roest, Ali Mesbah and Arie van Deursen. Regression Testing AJAX Applications: Coping with Dynamism. In Proceedings of the 3rd International Conference on Software Testing, Verification and Validation (ICST’10), Paris, France. IEEE Computer Society, 2010.
Group-wise partial least square regression
Camacho, José; Saccenti, Edoardo
2018-01-01
This paper introduces the group-wise partial least squares (GPLS) regression. GPLS is a new sparse PLS technique where the sparsity structure is defined in terms of groups of correlated variables, similarly to what is done in the related group-wise principal component analysis. These groups are
Functional data analysis of generalized regression quantiles
Guo, Mengmeng; Zhou, Lan; Huang, Jianhua Z.; Hä rdle, Wolfgang Karl
2013-01-01
Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.
Finite Algorithms for Robust Linear Regression
DEFF Research Database (Denmark)
Madsen, Kaj; Nielsen, Hans Bruun
1990-01-01
The Huber M-estimator for robust linear regression is analyzed. Newton type methods for solution of the problem are defined and analyzed, and finite convergence is proved. Numerical experiments with a large number of test problems demonstrate efficiency and indicate that this kind of approach may...
Function approximation with polynomial regression slines
International Nuclear Information System (INIS)
Urbanski, P.
1996-01-01
Principles of the polynomial regression splines as well as algorithms and programs for their computation are presented. The programs prepared using software package MATLAB are generally intended for approximation of the X-ray spectra and can be applied in the multivariate calibration of radiometric gauges. (author)
Assessing risk factors for periodontitis using regression
Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa
2013-10-01
Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.
Predicting Social Trust with Binary Logistic Regression
Adwere-Boamah, Joseph; Hufstedler, Shirley
2015-01-01
This study used binary logistic regression to predict social trust with five demographic variables from a national sample of adult individuals who participated in The General Social Survey (GSS) in 2012. The five predictor variables were respondents' highest degree earned, race, sex, general happiness and the importance of personally assisting…
Yet another look at MIDAS regression
Ph.H.B.F. Franses (Philip Hans)
2016-01-01
textabstractA MIDAS regression involves a dependent variable observed at a low frequency and independent variables observed at a higher frequency. This paper relates a true high frequency data generating process, where also the dependent variable is observed (hypothetically) at the high frequency,
Revisiting Regression in Autism: Heller's "Dementia Infantilis"
Westphal, Alexander; Schelinski, Stefanie; Volkmar, Fred; Pelphrey, Kevin
2013-01-01
Theodor Heller first described a severe regression of adaptive function in normally developing children, something he termed dementia infantilis, over one 100 years ago. Dementia infantilis is most closely related to the modern diagnosis, childhood disintegrative disorder. We translate Heller's paper, Uber Dementia Infantilis, and discuss…
Fast multi-output relevance vector regression
Ha, Youngmin
2017-01-01
This paper aims to decrease the time complexity of multi-output relevance vector regression from O(VM^3) to O(V^3+M^3), where V is the number of output dimensions, M is the number of basis functions, and V
Regression Equations for Birth Weight Estimation using ...
African Journals Online (AJOL)
In this study, Birth Weight has been estimated from anthropometric measurements of hand and foot. Linear regression equations were formed from each of the measured variables. These simple equations can be used to estimate Birth Weight of new born babies, in order to identify those with low birth weight and referred to ...
Superquantile Regression: Theory, Algorithms, and Applications
2014-12-01
Highway, Suite 1204, Arlington, Va 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington DC 20503. 1...Navy submariners, reliability engineering, uncertainty quantification, and financial risk management . Superquantile, superquantile regression...Royset Carlos F. Borges Associate Professor of Operations Research Dissertation Supervisor Professor of Applied Mathematics Lyn R. Whitaker Javier
Measurement Error in Education and Growth Regressions
Portela, M.; Teulings, C.N.; Alessie, R.
The perpetual inventory method used for the construction of education data per country leads to systematic measurement error. This paper analyses the effect of this measurement error on GDP regressions. There is a systematic difference in the education level between census data and observations
Measurement error in education and growth regressions
Portela, Miguel; Teulings, Coen; Alessie, R.
2004-01-01
The perpetual inventory method used for the construction of education data per country leads to systematic measurement error. This paper analyses the effect of this measurement error on GDP regressions. There is a systematic difference in the education level between census data and observations
Panel data specifications in nonparametric kernel regression
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...
transformation of independent variables in polynomial regression ...
African Journals Online (AJOL)
Ada
preferable when possible to work with a simple functional form in transformed variables rather than with a more complicated form in the original variables. In this paper, it is shown that linear transformations applied to independent variables in polynomial regression models affect the t ratio and hence the statistical ...
Multiple Linear Regression: A Realistic Reflector.
Nutt, A. T.; Batsell, R. R.
Examples of the use of Multiple Linear Regression (MLR) techniques are presented. This is done to show how MLR aids data processing and decision-making by providing the decision-maker with freedom in phrasing questions and by accurately reflecting the data on hand. A brief overview of the rationale underlying MLR is given, some basic definitions…
Weitzman, Vanessa N; Engmann, Lawrence; DiLuigi, Andrea; Maier, Donald; Nulsen, John; Benadiva, Claudio
2009-07-01
To compare IVF outcomes in poor-responder patients undergoing stimulation after luteal phase E(2) patch/GnRH antagonist (LPG) protocol versus microdose GnRH agonist protocol. Retrospective analysis. University-based IVF center. Forty-five women undergoing ovarian stimulation for IVF using the LPG protocol were compared with 76 women stimulated with the microdose GnRH agonist protocol from May 2005 to April 2006. Cancellation rate, number of oocytes retrieved, and clinical pregnancy rates. The mean number of oocytes (9.1 +/- 4.1 vs. 8.9 +/- 4.3) and mature oocytes (6.7 +/- 3.5 vs. 6.8 +/- 3.1) retrieved were similar, as were the fertilization rates (70.0% +/- 24.2% vs. 69.9% +/- 21.5%) and the number of embryos transferred (2.5 +/- 1.1 vs. 2.7 +/- 1.3). The cancellation rate was not significantly different between the groups (13/45, 28.9% vs. 23/76, 30.3%). Likewise, there were no significant differences among the implantation rate (15.0% vs. 12.5%), clinical pregnancy rate (43.3% vs. 45.1%), and ongoing pregnancy rate per transfer (33.3% vs. 26.0%) between both groups. This study demonstrates that the use of an E(2) patch and a GnRH antagonist during the preceding luteal phase in patients with a history of failed cycles can provide similar IVF outcomes when compared with the microdose GnRH agonist protocol.
Lin, Yingzhi; Deng, Xiangzheng; Li, Xing; Ma, Enjun
2014-12-01
Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.
Vaeth, Michael; Skovlund, Eva
2004-06-15
For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.
Controlling attribute effect in linear regression
Calders, Toon; Karim, Asim A.; Kamiran, Faisal; Ali, Wasif Mohammad; Zhang, Xiangliang
2013-01-01
In data mining we often have to learn from biased data, because, for instance, data comes from different batches or there was a gender or racial bias in the collection of social data. In some applications it may be necessary to explicitly control this bias in the models we learn from the data. This paper is the first to study learning linear regression models under constraints that control the biasing effect of a given attribute such as gender or batch number. We show how propensity modeling can be used for factoring out the part of the bias that can be justified by externally provided explanatory attributes. Then we analytically derive linear models that minimize squared error while controlling the bias by imposing constraints on the mean outcome or residuals of the models. Experiments with discrimination-aware crime prediction and batch effect normalization tasks show that the proposed techniques are successful in controlling attribute effects in linear regression models. © 2013 IEEE.
Stochastic development regression using method of moments
DEFF Research Database (Denmark)
Kühnel, Line; Sommer, Stefan Horst
2017-01-01
This paper considers the estimation problem arising when inferring parameters in the stochastic development regression model for manifold valued non-linear data. Stochastic development regression captures the relation between manifold-valued response and Euclidean covariate variables using...... the stochastic development construction. It is thereby able to incorporate several covariate variables and random effects. The model is intrinsically defined using the connection of the manifold, and the use of stochastic development avoids linearizing the geometry. We propose to infer parameters using...... the Method of Moments procedure that matches known constraints on moments of the observations conditional on the latent variables. The performance of the model is investigated in a simulation example using data on finite dimensional landmark manifolds....
Beta-binomial regression and bimodal utilization.
Liu, Chuan-Fen; Burgess, James F; Manning, Willard G; Maciejewski, Matthew L
2013-10-01
To illustrate how the analysis of bimodal U-shaped distributed utilization can be modeled with beta-binomial regression, which is rarely used in health services research. Veterans Affairs (VA) administrative data and Medicare claims in 2001-2004 for 11,123 Medicare-eligible VA primary care users in 2000. We compared means and distributions of VA reliance (the proportion of all VA/Medicare primary care visits occurring in VA) predicted from beta-binomial, binomial, and ordinary least-squares (OLS) models. Beta-binomial model fits the bimodal distribution of VA reliance better than binomial and OLS models due to the nondependence on normality and the greater flexibility in shape parameters. Increased awareness of beta-binomial regression may help analysts apply appropriate methods to outcomes with bimodal or U-shaped distributions. © Health Research and Educational Trust.
Testing homogeneity in Weibull-regression models.
Bolfarine, Heleno; Valença, Dione M
2005-10-01
In survival studies with families or geographical units it may be of interest testing whether such groups are homogeneous for given explanatory variables. In this paper we consider score type tests for group homogeneity based on a mixing model in which the group effect is modelled as a random variable. As opposed to hazard-based frailty models, this model presents survival times that conditioned on the random effect, has an accelerated failure time representation. The test statistics requires only estimation of the conventional regression model without the random effect and does not require specifying the distribution of the random effect. The tests are derived for a Weibull regression model and in the uncensored situation, a closed form is obtained for the test statistic. A simulation study is used for comparing the power of the tests. The proposed tests are applied to real data sets with censored data.
Are increases in cigarette taxation regressive?
Borren, P; Sutton, M
1992-12-01
Using the latest published data from Tobacco Advisory Council surveys, this paper re-evaluates the question of whether or not increases in cigarette taxation are regressive in the United Kingdom. The extended data set shows no evidence of increasing price-elasticity by social class as found in a major previous study. To the contrary, there appears to be no clear pattern in the price responsiveness of smoking behaviour across different social classes. Increases in cigarette taxation, while reducing smoking levels in all groups, fall most heavily on men and women in the lowest social class. Men and women in social class five can expect to pay eight and eleven times more of a tax increase respectively, than their social class one counterparts. Taken as a proportion of relative incomes, the regressive nature of increases in cigarette taxation is even more pronounced.
Controlling attribute effect in linear regression
Calders, Toon
2013-12-01
In data mining we often have to learn from biased data, because, for instance, data comes from different batches or there was a gender or racial bias in the collection of social data. In some applications it may be necessary to explicitly control this bias in the models we learn from the data. This paper is the first to study learning linear regression models under constraints that control the biasing effect of a given attribute such as gender or batch number. We show how propensity modeling can be used for factoring out the part of the bias that can be justified by externally provided explanatory attributes. Then we analytically derive linear models that minimize squared error while controlling the bias by imposing constraints on the mean outcome or residuals of the models. Experiments with discrimination-aware crime prediction and batch effect normalization tasks show that the proposed techniques are successful in controlling attribute effects in linear regression models. © 2013 IEEE.
Regression Models For Multivariate Count Data.
Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei
2017-01-01
Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.
Model selection in kernel ridge regression
DEFF Research Database (Denmark)
Exterkate, Peter
2013-01-01
Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...
Confidence bands for inverse regression models
International Nuclear Information System (INIS)
Birke, Melanie; Bissantz, Nicolai; Holzmann, Hajo
2010-01-01
We construct uniform confidence bands for the regression function in inverse, homoscedastic regression models with convolution-type operators. Here, the convolution is between two non-periodic functions on the whole real line rather than between two periodic functions on a compact interval, since the former situation arguably arises more often in applications. First, following Bickel and Rosenblatt (1973 Ann. Stat. 1 1071–95) we construct asymptotic confidence bands which are based on strong approximations and on a limit theorem for the supremum of a stationary Gaussian process. Further, we propose bootstrap confidence bands based on the residual bootstrap and prove consistency of the bootstrap procedure. A simulation study shows that the bootstrap confidence bands perform reasonably well for moderate sample sizes. Finally, we apply our method to data from a gel electrophoresis experiment with genetically engineered neuronal receptor subunits incubated with rat brain extract
Regressing Atherosclerosis by Resolving Plaque Inflammation
2017-07-01
regression requires the alteration of macrophages in the plaques to a tissue repair “alternatively” activated state. This switch in activation state... tissue repair “alternatively” activated state. This switch in activation state requires the action of TH2 cytokines interleukin (IL)-4 or IL-13. To...regulation of tissue macrophage and dendritic cell population dynamics by CSF-1. J Exp Med. 2011;208(9):1901–1916. 35. Xu H, Exner BG, Chilton PM
Determination of regression laws: Linear and nonlinear
International Nuclear Information System (INIS)
Onishchenko, A.M.
1994-01-01
A detailed mathematical determination of regression laws is presented in the article. Particular emphasis is place on determining the laws of X j on X l to account for source nuclei decay and detector errors in nuclear physics instrumentation. Both linear and nonlinear relations are presented. Linearization of 19 functions is tabulated, including graph, relation, variable substitution, obtained linear function, and remarks. 6 refs., 1 tab
Directional quantile regression in Octave (and MATLAB)
Czech Academy of Sciences Publication Activity Database
Boček, Pavel; Šiman, Miroslav
2016-01-01
Roč. 52, č. 1 (2016), s. 28-51 ISSN 0023-5954 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : quantile regression * multivariate quantile * depth contour * Matlab Subject RIV: IN - Informatics, Computer Science Impact factor: 0.379, year: 2016 http://library.utia.cas.cz/separaty/2016/SI/bocek-0458380.pdf
Logistic regression a self-learning text
Kleinbaum, David G
1994-01-01
This textbook provides students and professionals in the health sciences with a presentation of the use of logistic regression in research. The text is self-contained, and designed to be used both in class or as a tool for self-study. It arises from the author's many years of experience teaching this material and the notes on which it is based have been extensively used throughout the world.
Multitask Quantile Regression under the Transnormal Model.
Fan, Jianqing; Xue, Lingzhou; Zou, Hui
2016-01-01
We consider estimating multi-task quantile regression under the transnormal model, with focus on high-dimensional setting. We derive a surprisingly simple closed-form solution through rank-based covariance regularization. In particular, we propose the rank-based ℓ 1 penalization with positive definite constraints for estimating sparse covariance matrices, and the rank-based banded Cholesky decomposition regularization for estimating banded precision matrices. By taking advantage of alternating direction method of multipliers, nearest correlation matrix projection is introduced that inherits sampling properties of the unprojected one. Our work combines strengths of quantile regression and rank-based covariance regularization to simultaneously deal with nonlinearity and nonnormality for high-dimensional regression. Furthermore, the proposed method strikes a good balance between robustness and efficiency, achieves the "oracle"-like convergence rate, and provides the provable prediction interval under the high-dimensional setting. The finite-sample performance of the proposed method is also examined. The performance of our proposed rank-based method is demonstrated in a real application to analyze the protein mass spectroscopy data.
Complex regression Doppler optical coherence tomography
Elahi, Sahar; Gu, Shi; Thrane, Lars; Rollins, Andrew M.; Jenkins, Michael W.
2018-04-01
We introduce a new method to measure Doppler shifts more accurately and extend the dynamic range of Doppler optical coherence tomography (OCT). The two-point estimate of the conventional Doppler method is replaced with a regression that is applied to high-density B-scans in polar coordinates. We built a high-speed OCT system using a 1.68-MHz Fourier domain mode locked laser to acquire high-density B-scans (16,000 A-lines) at high enough frame rates (˜100 fps) to accurately capture the dynamics of the beating embryonic heart. Flow phantom experiments confirm that the complex regression lowers the minimum detectable velocity from 12.25 mm / s to 374 μm / s, whereas the maximum velocity of 400 mm / s is measured without phase wrapping. Complex regression Doppler OCT also demonstrates higher accuracy and precision compared with the conventional method, particularly when signal-to-noise ratio is low. The extended dynamic range allows monitoring of blood flow over several stages of development in embryos without adjusting the imaging parameters. In addition, applying complex averaging recovers hidden features in structural images.
Linear regression and the normality assumption.
Schmidt, Amand F; Finan, Chris
2017-12-16
Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and worse may bias model estimates. Linear regression assumptions are illustrated using simulated data and an empirical example on the relation between time since type 2 diabetes diagnosis and glycated hemoglobin levels. Simulation results were evaluated on coverage; i.e., the number of times the 95% confidence interval included the true slope coefficient. Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results. Contrary to this, assumptions on, the parametric model, absence of extreme observations, homoscedasticity, and independency of the errors, remain influential even in large sample size settings. Given that modern healthcare research typically includes thousands of subjects focusing on the normality assumption is often unnecessary, does not guarantee valid results, and worse may bias estimates due to the practice of outcome transformations. Copyright © 2017 Elsevier Inc. All rights reserved.
Satellite rainfall retrieval by logistic regression
Chiu, Long S.
1986-01-01
The potential use of logistic regression in rainfall estimation from satellite measurements is investigated. Satellite measurements provide covariate information in terms of radiances from different remote sensors.The logistic regression technique can effectively accommodate many covariates and test their significance in the estimation. The outcome from the logistical model is the probability that the rainrate of a satellite pixel is above a certain threshold. By varying the thresholds, a rainrate histogram can be obtained, from which the mean and the variant can be estimated. A logistical model is developed and applied to rainfall data collected during GATE, using as covariates the fractional rain area and a radiance measurement which is deduced from a microwave temperature-rainrate relation. It is demonstrated that the fractional rain area is an important covariate in the model, consistent with the use of the so-called Area Time Integral in estimating total rain volume in other studies. To calibrate the logistical model, simulated rain fields generated by rainfield models with prescribed parameters are needed. A stringent test of the logistical model is its ability to recover the prescribed parameters of simulated rain fields. A rain field simulation model which preserves the fractional rain area and lognormality of rainrates as found in GATE is developed. A stochastic regression model of branching and immigration whose solutions are lognormally distributed in some asymptotic limits has also been developed.
Bayesian Inference of a Multivariate Regression Model
Directory of Open Access Journals (Sweden)
Marick S. Sinay
2014-01-01
Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
Modeling oil production based on symbolic regression
International Nuclear Information System (INIS)
Yang, Guangfei; Li, Xianneng; Wang, Jianliang; Lian, Lian; Ma, Tieju
2015-01-01
Numerous models have been proposed to forecast the future trends of oil production and almost all of them are based on some predefined assumptions with various uncertainties. In this study, we propose a novel data-driven approach that uses symbolic regression to model oil production. We validate our approach on both synthetic and real data, and the results prove that symbolic regression could effectively identify the true models beneath the oil production data and also make reliable predictions. Symbolic regression indicates that world oil production will peak in 2021, which broadly agrees with other techniques used by researchers. Our results also show that the rate of decline after the peak is almost half the rate of increase before the peak, and it takes nearly 12 years to drop 4% from the peak. These predictions are more optimistic than those in several other reports, and the smoother decline will provide the world, especially the developing countries, with more time to orchestrate mitigation plans. -- Highlights: •A data-driven approach has been shown to be effective at modeling the oil production. •The Hubbert model could be discovered automatically from data. •The peak of world oil production is predicted to appear in 2021. •The decline rate after peak is half of the increase rate before peak. •Oil production projected to decline 4% post-peak
Face Alignment via Regressing Local Binary Features.
Ren, Shaoqing; Cao, Xudong; Wei, Yichen; Sun, Jian
2016-03-01
This paper presents a highly efficient and accurate regression approach for face alignment. Our approach has two novel components: 1) a set of local binary features and 2) a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. This approach achieves the state-of-the-art results when tested on the most challenging benchmarks to date. Furthermore, because extracting and regressing local binary features are computationally very cheap, our system is much faster than previous methods. It achieves over 3000 frames per second (FPS) on a desktop or 300 FPS on a mobile phone for locating a few dozens of landmarks. We also study a key issue that is important but has received little attention in the previous research, which is the face detector used to initialize alignment. We investigate several face detectors and perform quantitative evaluation on how they affect alignment accuracy. We find that an alignment friendly detector can further greatly boost the accuracy of our alignment method, reducing the error up to 16% relatively. To facilitate practical usage of face detection/alignment methods, we also propose a convenient metric to measure how good a detector is for alignment initialization.
Geographically weighted regression model on poverty indicator
Slamet, I.; Nugroho, N. F. T. A.; Muslich
2017-12-01
In this research, we applied geographically weighted regression (GWR) for analyzing the poverty in Central Java. We consider Gaussian Kernel as weighted function. The GWR uses the diagonal matrix resulted from calculating kernel Gaussian function as a weighted function in the regression model. The kernel weights is used to handle spatial effects on the data so that a model can be obtained for each location. The purpose of this paper is to model of poverty percentage data in Central Java province using GWR with Gaussian kernel weighted function and to determine the influencing factors in each regency/city in Central Java province. Based on the research, we obtained geographically weighted regression model with Gaussian kernel weighted function on poverty percentage data in Central Java province. We found that percentage of population working as farmers, population growth rate, percentage of households with regular sanitation, and BPJS beneficiaries are the variables that affect the percentage of poverty in Central Java province. In this research, we found the determination coefficient R2 are 68.64%. There are two categories of district which are influenced by different of significance factors.
Mixed-effects regression models in linguistics
Heylen, Kris; Geeraerts, Dirk
2018-01-01
When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addres...
On logistic regression analysis of dichotomized responses.
Lu, Kaifeng
2017-01-01
We study the properties of treatment effect estimate in terms of odds ratio at the study end point from logistic regression model adjusting for the baseline value when the underlying continuous repeated measurements follow a multivariate normal distribution. Compared with the analysis that does not adjust for the baseline value, the adjusted analysis produces a larger treatment effect as well as a larger standard error. However, the increase in standard error is more than offset by the increase in treatment effect so that the adjusted analysis is more powerful than the unadjusted analysis for detecting the treatment effect. On the other hand, the true adjusted odds ratio implied by the normal distribution of the underlying continuous variable is a function of the baseline value and hence is unlikely to be able to be adequately represented by a single value of adjusted odds ratio from the logistic regression model. In contrast, the risk difference function derived from the logistic regression model provides a reasonable approximation to the true risk difference function implied by the normal distribution of the underlying continuous variable over the range of the baseline distribution. We show that different metrics of treatment effect have similar statistical power when evaluated at the baseline mean. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
General regression and representation model for classification.
Directory of Open Access Journals (Sweden)
Jianjun Qian
Full Text Available Recently, the regularized coding-based classification methods (e.g. SRC and CRC show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients and the specific information (weight matrix of image pixels to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR and robust general regression and representation classifier (R-GRR. The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.
Image superresolution using support vector regression.
Ni, Karl S; Nguyen, Truong Q
2007-06-01
A thorough investigation of the application of support vector regression (SVR) to the superresolution problem is conducted through various frameworks. Prior to the study, the SVR problem is enhanced by finding the optimal kernel. This is done by formulating the kernel learning problem in SVR form as a convex optimization problem, specifically a semi-definite programming (SDP) problem. An additional constraint is added to reduce the SDP to a quadratically constrained quadratic programming (QCQP) problem. After this optimization, investigation of the relevancy of SVR to superresolution proceeds with the possibility of using a single and general support vector regression for all image content, and the results are impressive for small training sets. This idea is improved upon by observing structural properties in the discrete cosine transform (DCT) domain to aid in learning the regression. Further improvement involves a combination of classification and SVR-based techniques, extending works in resolution synthesis. This method, termed kernel resolution synthesis, uses specific regressors for isolated image content to describe the domain through a partitioned look of the vector space, thereby yielding good results.
International Nuclear Information System (INIS)
Jafri, Y.Z.; Kamal, L.
2007-01-01
Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)
Kempe, P T; van Oppen, P; de Haan, E; Twisk, J W R; Sluis, A; Smit, J H; van Dyck, R; van Balkom, A J L M
2007-09-01
Two methods for predicting remissions in obsessive-compulsive disorder (OCD) treatment are evaluated. Y-BOCS measurements of 88 patients with a primary OCD (DSM-III-R) diagnosis were performed over a 16-week treatment period, and during three follow-ups. Remission at any measurement was defined as a Y-BOCS score lower than thirteen combined with a reduction of seven points when compared with baseline. Logistic regression models were compared with a Cox regression for recurrent events model. Logistic regression yielded different models at different evaluation times. The recurrent events model remained stable when fewer measurements were used. Higher baseline levels of neuroticism and more severe OCD symptoms were associated with a lower chance of remission, early age of onset and more depressive symptoms with a higher chance. Choice of outcome time affects logistic regression prediction models. Recurrent events analysis uses all information on remissions and relapses. Short- and long-term predictors for OCD remission show overlap.
A method for nonlinear exponential regression analysis
Junkin, B. G.
1971-01-01
A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.
Multinomial logistic regression in workers' health
Grilo, Luís M.; Grilo, Helena L.; Gonçalves, Sónia P.; Junça, Ana
2017-11-01
In European countries, namely in Portugal, it is common to hear some people mentioning that they are exposed to excessive and continuous psychosocial stressors at work. This is increasing in diverse activity sectors, such as, the Services sector. A representative sample was collected from a Portuguese Services' organization, by applying a survey (internationally validated), which variables were measured in five ordered categories in Likert-type scale. A multinomial logistic regression model is used to estimate the probability of each category of the dependent variable general health perception where, among other independent variables, burnout appear as statistically significant.
Three Contributions to Robust Regression Diagnostics
Czech Academy of Sciences Publication Activity Database
Kalina, Jan
2015-01-01
Roč. 11, č. 2 (2015), s. 69-78 ISSN 1336-9180 Grant - others:GA ČR(CZ) GA13-01930S; Nadační fond na podporu vědy(CZ) Neuron Institutional support: RVO:67985807 Keywords : robust regression * robust econometrics * hypothesis test ing Subject RIV: BA - General Mathematics http://www.degruyter.com/view/j/jamsi.2015.11.issue-2/jamsi-2015-0013/jamsi-2015-0013.xml?format=INT
SDE based regression for random PDEs
Bayer, Christian
2016-01-01
A simulation based method for the numerical solution of PDE with random coefficients is presented. By the Feynman-Kac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead to an approximation of the global solution of the random PDE. We provide an initial error and complexity analysis of the proposed method along with numerical examples illustrating its behaviour.
Bayesian regression of piecewise homogeneous Poisson processes
Directory of Open Access Journals (Sweden)
Diego Sevilla
2015-12-01
Full Text Available In this paper, a Bayesian method for piecewise regression is adapted to handle counting processes data distributed as Poisson. A numerical code in Mathematica is developed and tested analyzing simulated data. The resulting method is valuable for detecting breaking points in the count rate of time series for Poisson processes. Received: 2 November 2015, Accepted: 27 November 2015; Edited by: R. Dickman; Reviewed by: M. Hutter, Australian National University, Canberra, Australia.; DOI: http://dx.doi.org/10.4279/PIP.070018 Cite as: D J R Sevilla, Papers in Physics 7, 070018 (2015
Selecting a Regression Saturated by Indicators
DEFF Research Database (Denmark)
Hendry, David F.; Johansen, Søren; Santos, Carlos
We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain the fin...... the finite-sample distribution of estimators of the mean and variance in a simple location-scale model under the null that no impulses matter. A Monte Carlo simulation confirms the null distribution, and shows power against an alternative of interest....
Selecting a Regression Saturated by Indicators
DEFF Research Database (Denmark)
Hendry, David F.; Johansen, Søren; Santos, Carlos
We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain the fin...... the finite-sample distribution of estimators of the mean and variance in a simple location-scale model under the null that no impulses matter. A Monte Carlo simulation confirms the null distribution, and shows power against an alternative of interest...
Mapping geogenic radon potential by regression kriging
Energy Technology Data Exchange (ETDEWEB)
Pásztor, László [Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest (Hungary); Szabó, Katalin Zsuzsanna, E-mail: sz_k_zs@yahoo.de [Department of Chemistry, Institute of Environmental Science, Szent István University, Páter Károly u. 1, Gödöllő 2100 (Hungary); Szatmári, Gábor; Laborczi, Annamária [Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest (Hungary); Horváth, Ákos [Department of Atomic Physics, Eötvös University, Pázmány Péter sétány 1/A, 1117 Budapest (Hungary)
2016-02-15
Radon ({sup 222}Rn) gas is produced in the radioactive decay chain of uranium ({sup 238}U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly. - Highlights: • A new method
Fixed kernel regression for voltammogram feature extraction
International Nuclear Information System (INIS)
Acevedo Rodriguez, F J; López-Sastre, R J; Gil-Jiménez, P; Maldonado Bascón, S; Ruiz-Reyes, N
2009-01-01
Cyclic voltammetry is an electroanalytical technique for obtaining information about substances under analysis without the need for complex flow systems. However, classifying the information in voltammograms obtained using this technique is difficult. In this paper, we propose the use of fixed kernel regression as a method for extracting features from these voltammograms, reducing the information to a few coefficients. The proposed approach has been applied to a wine classification problem with accuracy rates of over 98%. Although the method is described here for extracting voltammogram information, it can be used for other types of signals
Regression analysis for the social sciences
Gordon, Rachel A
2010-01-01
The book provides graduate students in the social sciences with the basic skills that they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of both SAS and Stata "side-by-side" and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set.
SDE based regression for random PDEs
Bayer, Christian
2016-01-06
A simulation based method for the numerical solution of PDE with random coefficients is presented. By the Feynman-Kac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead to an approximation of the global solution of the random PDE. We provide an initial error and complexity analysis of the proposed method along with numerical examples illustrating its behaviour.
Neutrosophic Correlation and Simple Linear Regression
Directory of Open Access Journals (Sweden)
A. A. Salama
2014-09-01
Full Text Available Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache. Recently, Salama et al., introduced the concept of correlation coefficient of neutrosophic data. In this paper, we introduce and study the concepts of correlation and correlation coefficient of neutrosophic data in probability spaces and study some of their properties. Also, we introduce and study the neutrosophic simple linear regression model. Possible applications to data processing are touched upon.
Spectral density regression for bivariate extremes
Castro Camilo, Daniela
2016-05-11
We introduce a density regression model for the spectral density of a bivariate extreme value distribution, that allows us to assess how extremal dependence can change over a covariate. Inference is performed through a double kernel estimator, which can be seen as an extension of the Nadaraya–Watson estimator where the usual scalar responses are replaced by mean constrained densities on the unit interval. Numerical experiments with the methods illustrate their resilience in a variety of contexts of practical interest. An extreme temperature dataset is used to illustrate our methods. © 2016 Springer-Verlag Berlin Heidelberg
SPE dose prediction using locally weighted regression
International Nuclear Information System (INIS)
Hines, J. W.; Townsend, L. W.; Nichols, T. F.
2005-01-01
When astronauts are outside earth's protective magnetosphere, they are subject to large radiation doses resulting from solar particle events (SPEs). The total dose received from a major SPE in deep space could cause severe radiation poisoning. The dose is usually received over a 20-40 h time interval but the event's effects may be mitigated with an early warning system. This paper presents a method to predict the total dose early in the event. It uses a locally weighted regression model, which is easier to train and provides predictions as accurate as neural network models previously used. (authors)
Mapping geogenic radon potential by regression kriging
International Nuclear Information System (INIS)
Pásztor, László; Szabó, Katalin Zsuzsanna; Szatmári, Gábor; Laborczi, Annamária; Horváth, Ákos
2016-01-01
Radon ( 222 Rn) gas is produced in the radioactive decay chain of uranium ( 238 U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly. - Highlights: • A new method, regression
SPE dose prediction using locally weighted regression
International Nuclear Information System (INIS)
Hines, J. W.; Townsend, L. W.; Nichols, T. F.
2005-01-01
When astronauts are outside Earth's protective magnetosphere, they are subject to large radiation doses resulting from solar particle events. The total dose received from a major solar particle event in deep space could cause severe radiation poisoning. The dose is usually received over a 20-40 h time interval but the event's effects may be reduced with an early warning system. This paper presents a method to predict the total dose early in the event. It uses a locally weighted regression model, which is easier to train, and provides predictions as accurate as the neural network models that were used previously. (authors)
AIRLINE ACTIVITY FORECASTING BY REGRESSION MODELS
Directory of Open Access Journals (Sweden)
Н. Білак
2012-04-01
Full Text Available Proposed linear and nonlinear regression models, which take into account the equation of trend and seasonality indices for the analysis and restore the volume of passenger traffic over the past period of time and its prediction for future years, as well as the algorithm of formation of these models based on statistical analysis over the years. The desired model is the first step for the synthesis of more complex models, which will enable forecasting of passenger (income level airline with the highest accuracy and time urgency.
Logistic regression applied to natural hazards: rare event logistic regression with replications
Guns, M.; Vanacker, Veerle
2012-01-01
Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logisti...
Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon
2015-01-01
Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.
Bayesian nonlinear regression for large small problems
Chakraborty, Sounak; Ghosh, Malay; Mallick, Bani K.
2012-01-01
Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large p small n problem. Furthermore, the problem is more complicated when we have multiple correlated responses. We develop multivariate nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik's ε-insensitive loss function, based on reproducing kernel Hilbert spaces (RKHS) under the multivariate correlated response setup. This provides a full probabilistic description of support vector machine (SVM) rather than an algorithm for fitting purposes. We have also introduced a multivariate version of the relevance vector machine (RVM). Instead of the original treatment of the RVM relying on the use of type II maximum likelihood estimates of the hyper-parameters, we put a prior on the hyper-parameters and use Markov chain Monte Carlo technique for computation. We have also proposed an empirical Bayes method for our RVM and SVM. Our methods are illustrated with a prediction problem in the near-infrared (NIR) spectroscopy. A simulation study is also undertaken to check the prediction accuracy of our models. © 2012 Elsevier Inc.
Spontaneous regression of intracranial malignant lymphoma
International Nuclear Information System (INIS)
Kojo, Nobuto; Tokutomi, Takashi; Eguchi, Gihachirou; Takagi, Shigeyuki; Matsumoto, Tomie; Sasaguri, Yasuyuki; Shigemori, Minoru.
1988-01-01
In a 46-year-old female with a 1-month history of gait and speech disturbances, computed tomography (CT) demonstrated mass lesions of slightly high density in the left basal ganglia and left frontal lobe. The lesions were markedly enhanced by contrast medium. The patient received no specific treatment, but her clinical manifestations gradually abated and the lesions decreased in size. Five months after her initial examination, the lesions were absent on CT scans; only a small area of low density remained. Residual clinical symptoms included mild right hemiparesis and aphasia. After 14 months the patient again deteriorated, and a CT scan revealed mass lesions in the right frontal lobe and the pons. However, no enhancement was observed in the previously affected regions. A biopsy revealed malignant lymphoma. Despite treatment with steroids and radiation, the patient's clinical status progressively worsened and she died 27 months after initial presentation. Seven other cases of spontaneous regression of primary malignant lymphoma have been reported. In this case, the mechanism of the spontaneous regression was not clear, but changes in immunologic status may have been involved. (author)
Regression testing in the TOTEM DCS
International Nuclear Information System (INIS)
Rodríguez, F Lucas; Atanassov, I; Burkimsher, P; Frost, O; Taskinen, J; Tulimaki, V
2012-01-01
The Detector Control System of the TOTEM experiment at the LHC is built with the industrial product WinCC OA (PVSS). The TOTEM system is generated automatically through scripts using as input the detector Product Breakdown Structure (PBS) structure and its pinout connectivity, archiving and alarm metainformation, and some other heuristics based on the naming conventions. When those initial parameters and automation code are modified to include new features, the resulting PVSS system can also introduce side-effects. On a daily basis, a custom developed regression testing tool takes the most recent code from a Subversion (SVN) repository and builds a new control system from scratch. This system is exported in plain text format using the PVSS export tool, and compared with a system previously validated by a human. A report is sent to the developers with any differences highlighted, in readiness for validation and acceptance as a new stable version. This regression approach is not dependent on any development framework or methodology. This process has been satisfactory during several months, proving to be a very valuable tool before deploying new versions in the production systems.
Supporting Regularized Logistic Regression Privately and Efficiently
Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei
2016-01-01
As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc. PMID:27271738
Structural Break Tests Robust to Regression Misspecification
Directory of Open Access Journals (Sweden)
Alaa Abi Morshed
2018-05-01
Full Text Available Structural break tests for regression models are sensitive to model misspecification. We show—analytically and through simulations—that the sup Wald test for breaks in the conditional mean and variance of a time series process exhibits severe size distortions when the conditional mean dynamics are misspecified. We also show that the sup Wald test for breaks in the unconditional mean and variance does not have the same size distortions, yet benefits from similar power to its conditional counterpart in correctly specified models. Hence, we propose using it as an alternative and complementary test for breaks. We apply the unconditional and conditional mean and variance tests to three US series: unemployment, industrial production growth and interest rates. Both the unconditional and the conditional mean tests detect a break in the mean of interest rates. However, for the other two series, the unconditional mean test does not detect a break, while the conditional mean tests based on dynamic regression models occasionally detect a break, with the implied break-point estimator varying across different dynamic specifications. For all series, the unconditional variance does not detect a break while most tests for the conditional variance do detect a break which also varies across specifications.
Supporting Regularized Logistic Regression Privately and Efficiently.
Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei
2016-01-01
As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.
Bayesian nonlinear regression for large small problems
Chakraborty, Sounak
2012-07-01
Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large p small n problem. Furthermore, the problem is more complicated when we have multiple correlated responses. We develop multivariate nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik\\'s ε-insensitive loss function, based on reproducing kernel Hilbert spaces (RKHS) under the multivariate correlated response setup. This provides a full probabilistic description of support vector machine (SVM) rather than an algorithm for fitting purposes. We have also introduced a multivariate version of the relevance vector machine (RVM). Instead of the original treatment of the RVM relying on the use of type II maximum likelihood estimates of the hyper-parameters, we put a prior on the hyper-parameters and use Markov chain Monte Carlo technique for computation. We have also proposed an empirical Bayes method for our RVM and SVM. Our methods are illustrated with a prediction problem in the near-infrared (NIR) spectroscopy. A simulation study is also undertaken to check the prediction accuracy of our models. © 2012 Elsevier Inc.
Hyperspectral Unmixing with Robust Collaborative Sparse Regression
Directory of Open Access Journals (Sweden)
Chang Li
2016-07-01
Full Text Available Recently, sparse unmixing (SU of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM, which ignores the possible nonlinear effects (i.e., nonlinearity. In this paper, we propose a new method named robust collaborative sparse regression (RCSR based on the robust LMM (rLMM for hyperspectral unmixing. The rLMM takes the nonlinearity into consideration, and the nonlinearity is merely treated as outlier, which has the underlying sparse property. The RCSR simultaneously takes the collaborative sparse property of the abundance and sparsely distributed additive property of the outlier into consideration, which can be formed as a robust joint sparse regression problem. The inexact augmented Lagrangian method (IALM is used to optimize the proposed RCSR. The qualitative and quantitative experiments on synthetic datasets and real hyperspectral images demonstrate that the proposed RCSR is efficient for solving the hyperspectral SU problem compared with the other four state-of-the-art algorithms.
Supporting Regularized Logistic Regression Privately and Efficiently.
Directory of Open Access Journals (Sweden)
Wenfa Li
Full Text Available As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.
Nishimura, Ryo; Okuda, Kiyoshi
2015-01-01
There is increasing interest in the role of oxygen conditions in the microenvironment of organs because of the discovery of a hypoxia-specific transcription factor, namely hypoxia-inducible factor (HIF) 1. Ovarian function has several phases that change day by day, including ovulation, follicular growth and corpus luteum formation and regression. These phases are regulated by many factors, including pituitary hormones and local hormones, such as steroids, peptides and cytokines, as well as ox...
Interpreting parameters in the logistic regression model with random effects
DEFF Research Database (Denmark)
Larsen, Klaus; Petersen, Jørgen Holm; Budtz-Jørgensen, Esben
2000-01-01
interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects......interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects...
Directory of Open Access Journals (Sweden)
Larissa Almenara Silva dos Santos
2011-04-01
percentage of women with above-average body water during the luteal phase (77%. The consumption of foods from the complementary group was higher during the luteal phase. The consumption of foods from all other groups during both phases was below the recommended levels, except for meats. Food cravings were mild during the entire menstrual cycle and there were no significant differences between the phases. Food cravings were positively associated with increased intake of foods from the complementary group. CONCLUSION: In healthy women, the menstrual cycle influences food consumption and the luteal phase causes water retention.
BANK FAILURE PREDICTION WITH LOGISTIC REGRESSION
Directory of Open Access Journals (Sweden)
Taha Zaghdoudi
2013-04-01
Full Text Available In recent years the economic and financial world is shaken by a wave of financial crisis and resulted in violent bank fairly huge losses. Several authors have focused on the study of the crises in order to develop an early warning model. It is in the same path that our work takes its inspiration. Indeed, we have tried to develop a predictive model of Tunisian bank failures with the contribution of the binary logistic regression method. The specificity of our prediction model is that it takes into account microeconomic indicators of bank failures. The results obtained using our provisional model show that a bank's ability to repay its debt, the coefficient of banking operations, bank profitability per employee and leverage financial ratio has a negative impact on the probability of failure.
Robust Mediation Analysis Based on Median Regression
Yuan, Ying; MacKinnon, David P.
2014-01-01
Mediation analysis has many applications in psychology and the social sciences. The most prevalent methods typically assume that the error distribution is normal and homoscedastic. However, this assumption may rarely be met in practice, which can affect the validity of the mediation analysis. To address this problem, we propose robust mediation analysis based on median regression. Our approach is robust to various departures from the assumption of homoscedasticity and normality, including heavy-tailed, skewed, contaminated, and heteroscedastic distributions. Simulation studies show that under these circumstances, the proposed method is more efficient and powerful than standard mediation analysis. We further extend the proposed robust method to multilevel mediation analysis, and demonstrate through simulation studies that the new approach outperforms the standard multilevel mediation analysis. We illustrate the proposed method using data from a program designed to increase reemployment and enhance mental health of job seekers. PMID:24079925
ANYOLS, Least Square Fit by Stepwise Regression
International Nuclear Information System (INIS)
Atwoods, C.L.; Mathews, S.
1986-01-01
Description of program or function: ANYOLS is a stepwise program which fits data using ordinary or weighted least squares. Variables are selected for the model in a stepwise way based on a user- specified input criterion or a user-written subroutine. The order in which variables are entered can be influenced by user-defined forcing priorities. Instead of stepwise selection, ANYOLS can try all possible combinations of any desired subset of the variables. Automatic output for the final model in a stepwise search includes plots of the residuals, 'studentized' residuals, and leverages; if the model is not too large, the output also includes partial regression and partial leverage plots. A data set may be re-used so that several selection criteria can be tried. Flexibility is increased by allowing the substitution of user-written subroutines for several default subroutines
Nonparametric additive regression for repeatedly measured data
Carroll, R. J.
2009-05-20
We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology. © 2009 Biometrika Trust.
Conjoined legs: Sirenomelia or caudal regression syndrome?
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Sakti Prasad Das
2013-01-01
Full Text Available Presence of single umbilical persistent vitelline artery distinguishes sirenomelia from caudal regression syndrome. We report a case of a12-year-old boy who had bilateral umbilical arteries presented with fusion of both legs in the lower one third of leg. Both feet were rudimentary. The right foot had a valgus rocker-bottom deformity. All toes were present but rudimentary. The left foot showed absence of all toes. Physical examination showed left tibia vara. The chest evaluation in sitting revealed pigeon chest and elevated right shoulder. Posterior examination of the trunk showed thoracic scoliosis with convexity to right. The patient was operated and at 1 year followup the boy had two separate legs with a good aesthetic and functional results.
Conjoined legs: Sirenomelia or caudal regression syndrome?
Das, Sakti Prasad; Ojha, Niranjan; Ganesh, G Shankar; Mohanty, Ram Narayan
2013-07-01
Presence of single umbilical persistent vitelline artery distinguishes sirenomelia from caudal regression syndrome. We report a case of a12-year-old boy who had bilateral umbilical arteries presented with fusion of both legs in the lower one third of leg. Both feet were rudimentary. The right foot had a valgus rocker-bottom deformity. All toes were present but rudimentary. The left foot showed absence of all toes. Physical examination showed left tibia vara. The chest evaluation in sitting revealed pigeon chest and elevated right shoulder. Posterior examination of the trunk showed thoracic scoliosis with convexity to right. The patient was operated and at 1 year followup the boy had two separate legs with a good aesthetic and functional results.
Logistic regression against a divergent Bayesian network
Directory of Open Access Journals (Sweden)
Noel Antonio Sánchez Trujillo
2015-01-01
Full Text Available This article is a discussion about two statistical tools used for prediction and causality assessment: logistic regression and Bayesian networks. Using data of a simulated example from a study assessing factors that might predict pulmonary emphysema (where fingertip pigmentation and smoking are considered; we posed the following questions. Is pigmentation a confounding, causal or predictive factor? Is there perhaps another factor, like smoking, that confounds? Is there a synergy between pigmentation and smoking? The results, in terms of prediction, are similar with the two techniques; regarding causation, differences arise. We conclude that, in decision-making, the sum of both: a statistical tool, used with common sense, and previous evidence, taking years or even centuries to develop; is better than the automatic and exclusive use of statistical resources.
Adaptive regression for modeling nonlinear relationships
Knafl, George J
2016-01-01
This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the s...
Crime Modeling using Spatial Regression Approach
Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.
2018-01-01
Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.
Regression analysis for the social sciences
Gordon, Rachel A
2015-01-01
Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.
Entrepreneurial intention modeling using hierarchical multiple regression
Directory of Open Access Journals (Sweden)
Marina Jeger
2014-12-01
Full Text Available The goal of this study is to identify the contribution of effectuation dimensions to the predictive power of the entrepreneurial intention model over and above that which can be accounted for by other predictors selected and confirmed in previous studies. As is often the case in social and behavioral studies, some variables are likely to be highly correlated with each other. Therefore, the relative amount of variance in the criterion variable explained by each of the predictors depends on several factors such as the order of variable entry and sample specifics. The results show the modest predictive power of two dimensions of effectuation prior to the introduction of the theory of planned behavior elements. The article highlights the main advantages of applying hierarchical regression in social sciences as well as in the specific context of entrepreneurial intention formation, and addresses some of the potential pitfalls that this type of analysis entails.
Gaussian process regression for geometry optimization
Denzel, Alexander; Kästner, Johannes
2018-03-01
We implemented a geometry optimizer based on Gaussian process regression (GPR) to find minimum structures on potential energy surfaces. We tested both a two times differentiable form of the Matérn kernel and the squared exponential kernel. The Matérn kernel performs much better. We give a detailed description of the optimization procedures. These include overshooting the step resulting from GPR in order to obtain a higher degree of interpolation vs. extrapolation. In a benchmark against the Limited-memory Broyden-Fletcher-Goldfarb-Shanno optimizer of the DL-FIND library on 26 test systems, we found the new optimizer to generally reduce the number of required optimization steps.
Least square regularized regression in sum space.
Xu, Yong-Li; Chen, Di-Rong; Li, Han-Xiong; Liu, Lu
2013-04-01
This paper proposes a least square regularized regression algorithm in sum space of reproducing kernel Hilbert spaces (RKHSs) for nonflat function approximation, and obtains the solution of the algorithm by solving a system of linear equations. This algorithm can approximate the low- and high-frequency component of the target function with large and small scale kernels, respectively. The convergence and learning rate are analyzed. We measure the complexity of the sum space by its covering number and demonstrate that the covering number can be bounded by the product of the covering numbers of basic RKHSs. For sum space of RKHSs with Gaussian kernels, by choosing appropriate parameters, we tradeoff the sample error and regularization error, and obtain a polynomial learning rate, which is better than that in any single RKHS. The utility of this method is illustrated with two simulated data sets and five real-life databases.
Statistical learning from a regression perspective
Berk, Richard A
2016-01-01
This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this can be seen as an extension of nonparametric regression. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. A continued emphasis on the implications for practice runs through the text. Among the statistical learning procedures examined are bagging, random forests, boosting, support vector machines and neural networks. Response variables may be quantitative or categorical. As in the first edition, a unifying theme is supervised learning that can be trea...
Model Selection in Kernel Ridge Regression
DEFF Research Database (Denmark)
Exterkate, Peter
Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels......, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based...... on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely...
Learning Inverse Rig Mappings by Nonlinear Regression.
Holden, Daniel; Saito, Jun; Komura, Taku
2017-03-01
We present a framework to design inverse rig-functions-functions that map low level representations of a character's pose such as joint positions or surface geometry to the representation used by animators called the animation rig. Animators design scenes using an animation rig, a framework widely adopted in animation production which allows animators to design character poses and geometry via intuitive parameters and interfaces. Yet most state-of-the-art computer animation techniques control characters through raw, low level representations such as joint angles, joint positions, or vertex coordinates. This difference often stops the adoption of state-of-the-art techniques in animation production. Our framework solves this issue by learning a mapping between the low level representations of the pose and the animation rig. We use nonlinear regression techniques, learning from example animation sequences designed by the animators. When new motions are provided in the skeleton space, the learned mapping is used to estimate the rig controls that reproduce such a motion. We introduce two nonlinear functions for producing such a mapping: Gaussian process regression and feedforward neural networks. The appropriate solution depends on the nature of the rig and the amount of data available for training. We show our framework applied to various examples including articulated biped characters, quadruped characters, facial animation rigs, and deformable characters. With our system, animators have the freedom to apply any motion synthesis algorithm to arbitrary rigging and animation pipelines for immediate editing. This greatly improves the productivity of 3D animation, while retaining the flexibility and creativity of artistic input.
DRREP: deep ridge regressed epitope predictor.
Sher, Gene; Zhi, Degui; Zhang, Shaojie
2017-10-03
The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP). DRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702. DRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.
Collaborative regression-based anatomical landmark detection
International Nuclear Information System (INIS)
Gao, Yaozong; Shen, Dinggang
2015-01-01
Anatomical landmark detection plays an important role in medical image analysis, e.g. for registration, segmentation and quantitative analysis. Among the various existing methods for landmark detection, regression-based methods have recently attracted much attention due to their robustness and efficiency. In these methods, landmarks are localised through voting from all image voxels, which is completely different from the classification-based methods that use voxel-wise classification to detect landmarks. Despite their robustness, the accuracy of regression-based landmark detection methods is often limited due to (1) the inclusion of uninformative image voxels in the voting procedure, and (2) the lack of effective ways to incorporate inter-landmark spatial dependency into the detection step. In this paper, we propose a collaborative landmark detection framework to address these limitations. The concept of collaboration is reflected in two aspects. (1) Multi-resolution collaboration. A multi-resolution strategy is proposed to hierarchically localise landmarks by gradually excluding uninformative votes from faraway voxels. Moreover, for informative voxels near the landmark, a spherical sampling strategy is also designed at the training stage to improve their prediction accuracy. (2) Inter-landmark collaboration. A confidence-based landmark detection strategy is proposed to improve the detection accuracy of ‘difficult-to-detect’ landmarks by using spatial guidance from ‘easy-to-detect’ landmarks. To evaluate our method, we conducted experiments extensively on three datasets for detecting prostate landmarks and head and neck landmarks in computed tomography images, and also dental landmarks in cone beam computed tomography images. The results show the effectiveness of our collaborative landmark detection framework in improving landmark detection accuracy, compared to other state-of-the-art methods. (paper)
Pye, Clare; Chatters, Robin; Cohen, Judith; Brian, Kate; Cheong, Ying C; Laird, Susan; Mohiyiddeen, Lamiya; Skull, Jonathan; Walters, Stephen; Young, Tracey; Metwally, Mostafa
2018-05-20
Endometrial trauma commonly known as endometrial scratch (ES) has been shown to improve pregnancy rates in women with a history of repeated implantation failure undergoing in vitro fertilisation (IVF), with or without intracytoplasmic sperm injection (ICSI). However, the procedure has not yet been fully explored in women having IVF/ICSI for the first time. This study aims to examine the effect of performing an ES in the mid-luteal phase prior to a first-time IVF/ICSI cycle on the chances of achieving a clinical pregnancy and live birth. If ES can influence this success rate, there would be a significant cost saving to the National Health Service through decreasing the number of IVF/ICSI cycles necessary to achieve a pregnancy, increase the practice of single embryo transfer and consequently have a large impact on risks and costs associated with multiple pregnancies. This 30-month, UK, multicentre, parallel group, randomised controlled trial includes a 9-month internal pilot and health economic analysis recruiting 1044 women from 16 fertility units. It will follow up participants to identify if IVF/ICSI has been successful and live birth has occurred up to 6 weeks post partum. Primary analysis will be on an intention-to-treat basis. A substudy of endometrial samples obtained during the ES will assess the role of immune factors in embryo implantation. Main trial recruitment commenced on January 2017 and is ongoing.Participants randomised to the intervention group will receive the ES procedure in the mid-luteal phase of the preceding cycle prior to first-time IVF/ICSI treatment versus usual IVF/ICSI treatment in the control group, with 1:1 randomisation. The primary outcome is live birth rate after completed 24 weeks gestation. South Central-Berkshire Research Ethics Committee approved the protocol. Findings will be submitted to peer-reviewed journals and abstracts to relevant national and international conferences. ISRCTN23800982; Pre-results. © Article author
Logistic regression applied to natural hazards: rare event logistic regression with replications
Directory of Open Access Journals (Sweden)
M. Guns
2012-06-01
Full Text Available Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.
Logistic regression applied to natural hazards: rare event logistic regression with replications
Guns, M.; Vanacker, V.
2012-06-01
Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.
Smith, Paul F; Ganesh, Siva; Liu, Ping
2013-10-30
Regression is a common statistical tool for prediction in neuroscience. However, linear regression is by far the most common form of regression used, with regression trees receiving comparatively little attention. In this study, the results of conventional multiple linear regression (MLR) were compared with those of random forest regression (RFR), in the prediction of the concentrations of 9 neurochemicals in the vestibular nucleus complex and cerebellum that are part of the l-arginine biochemical pathway (agmatine, putrescine, spermidine, spermine, l-arginine, l-ornithine, l-citrulline, glutamate and γ-aminobutyric acid (GABA)). The R(2) values for the MLRs were higher than the proportion of variance explained values for the RFRs: 6/9 of them were ≥ 0.70 compared to 4/9 for RFRs. Even the variables that had the lowest R(2) values for the MLRs, e.g. ornithine (0.50) and glutamate (0.61), had much lower proportion of variance explained values for the RFRs (0.27 and 0.49, respectively). The RSE values for the MLRs were lower than those for the RFRs in all but two cases. In general, MLRs seemed to be superior to the RFRs in terms of predictive value and error. In the case of this data set, MLR appeared to be superior to RFR in terms of its explanatory value and error. This result suggests that MLR may have advantages over RFR for prediction in neuroscience with this kind of data set, but that RFR can still have good predictive value in some cases. Copyright © 2013 Elsevier B.V. All rights reserved.
Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation
Directory of Open Access Journals (Sweden)
Sharad Damodar Gore
2009-10-01
Full Text Available Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR. This estimator is obtained from unbiased ridge regression (URR in the same way that ordinary ridge regression (ORR is obtained from ordinary least squares (OLS. Properties of MUR are derived. Results on its matrix mean squared error (MMSE are obtained. MUR is compared with ORR and URR in terms of MMSE. These results are illustrated with an example based on data generated by Hoerl and Kennard (1975.
Directory of Open Access Journals (Sweden)
Hong-Juan Li
2013-04-01
Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients
Gorgees, HazimMansoor; Mahdi, FatimahAssim
2018-05-01
This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.
AN APPLICATION OF FUNCTIONAL MULTIVARIATE REGRESSION MODEL TO MULTICLASS CLASSIFICATION
Krzyśko, Mirosław; Smaga, Łukasz
2017-01-01
In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed ...
Spatial vulnerability assessments by regression kriging
Pásztor, László; Laborczi, Annamária; Takács, Katalin; Szatmári, Gábor
2016-04-01
information representing IEW or GRP forming environmental factors were taken into account to support the spatial inference of the locally experienced IEW frequency and measured GRP values respectively. An efficient spatial prediction methodology was applied to construct reliable maps, namely regression kriging (RK) using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Application of RK also provides the possibility of inherent accuracy assessment. The resulting maps are characterized by global and local measures of its accuracy. Additionally the method enables interval estimation for spatial extension of the areas of predefined risk categories. All of these outputs provide useful contribution to spatial planning, action planning and decision making. Acknowledgement: Our work was partly supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).
Introduction to the use of regression models in epidemiology.
Bender, Ralf
2009-01-01
Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.
Automation of Flight Software Regression Testing
Tashakkor, Scott B.
2016-01-01
NASA is developing the Space Launch System (SLS) to be a heavy lift launch vehicle supporting human and scientific exploration beyond earth orbit. SLS will have a common core stage, an upper stage, and different permutations of boosters and fairings to perform various crewed or cargo missions. Marshall Space Flight Center (MSFC) is writing the Flight Software (FSW) that will operate the SLS launch vehicle. The FSW is developed in an incremental manner based on "Agile" software techniques. As the FSW is incrementally developed, testing the functionality of the code needs to be performed continually to ensure that the integrity of the software is maintained. Manually testing the functionality on an ever-growing set of requirements and features is not an efficient solution and therefore needs to be done automatically to ensure testing is comprehensive. To support test automation, a framework for a regression test harness has been developed and used on SLS FSW. The test harness provides a modular design approach that can compile or read in the required information specified by the developer of the test. The modularity provides independence between groups of tests and the ability to add and remove tests without disturbing others. This provides the SLS FSW team a time saving feature that is essential to meeting SLS Program technical and programmatic requirements. During development of SLS FSW, this technique has proved to be a useful tool to ensure all requirements have been tested, and that desired functionality is maintained, as changes occur. It also provides a mechanism for developers to check functionality of the code that they have developed. With this system, automation of regression testing is accomplished through a scheduling tool and/or commit hooks. Key advantages of this test harness capability includes execution support for multiple independent test cases, the ability for developers to specify precisely what they are testing and how, the ability to add
Laplacian embedded regression for scalable manifold regularization.
Chen, Lin; Tsang, Ivor W; Xu, Dong
2012-06-01
Semi-supervised learning (SSL), as a powerful tool to learn from a limited number of labeled data and a large number of unlabeled data, has been attracting increasing attention in the machine learning community. In particular, the manifold regularization framework has laid solid theoretical foundations for a large family of SSL algorithms, such as Laplacian support vector machine (LapSVM) and Laplacian regularized least squares (LapRLS). However, most of these algorithms are limited to small scale problems due to the high computational cost of the matrix inversion operation involved in the optimization problem. In this paper, we propose a novel framework called Laplacian embedded regression by introducing an intermediate decision variable into the manifold regularization framework. By using ∈-insensitive loss, we obtain the Laplacian embedded support vector regression (LapESVR) algorithm, which inherits the sparse solution from SVR. Also, we derive Laplacian embedded RLS (LapERLS) corresponding to RLS under the proposed framework. Both LapESVR and LapERLS possess a simpler form of a transformed kernel, which is the summation of the original kernel and a graph kernel that captures the manifold structure. The benefits of the transformed kernel are two-fold: (1) we can deal with the original kernel matrix and the graph Laplacian matrix in the graph kernel separately and (2) if the graph Laplacian matrix is sparse, we only need to perform the inverse operation for a sparse matrix, which is much more efficient when compared with that for a dense one. Inspired by kernel principal component analysis, we further propose to project the introduced decision variable into a subspace spanned by a few eigenvectors of the graph Laplacian matrix in order to better reflect the data manifold, as well as accelerate the calculation of the graph kernel, allowing our methods to efficiently and effectively cope with large scale SSL problems. Extensive experiments on both toy and real
Directory of Open Access Journals (Sweden)
Qiutong Jin
2016-06-01
Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.
Hecht, Jeffrey B.
The analysis of regression residuals and detection of outliers are discussed, with emphasis on determining how deviant an individual data point must be to be considered an outlier and the impact that multiple suspected outlier data points have on the process of outlier determination and treatment. Only bivariate (one dependent and one independent)…
A rotor optimization using regression analysis
Giansante, N.
1984-01-01
The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.
Regression analysis of sparse asynchronous longitudinal data.
Cao, Hongyuan; Zeng, Donglin; Fine, Jason P
2015-09-01
We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time-dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus.
Free Software Development. 1. Fitting Statistical Regressions
Directory of Open Access Journals (Sweden)
Lorentz JÄNTSCHI
2002-12-01
Full Text Available The present paper is focused on modeling of statistical data processing with applications in field of material science and engineering. A new method of data processing is presented and applied on a set of 10 Ni–Mn–Ga ferromagnetic ordered shape memory alloys that are known to exhibit phonon softening and soft mode condensation into a premartensitic phase prior to the martensitic transformation itself. The method allows to identify the correlations between data sets and to exploit them later in statistical study of alloys. An algorithm for computing data was implemented in preprocessed hypertext language (PHP, a hypertext markup language interface for them was also realized and put onto comp.east.utcluj.ro educational web server, and it is accessible via http protocol at the address http://vl.academicdirect.ro/applied_statistics/linear_regression/multiple/v1.5/. The program running for the set of alloys allow to identify groups of alloys properties and give qualitative measure of correlations between properties. Surfaces of property dependencies are also fitted.
Kepler AutoRegressive Planet Search (KARPS)
Caceres, Gabriel
2018-01-01
One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The Kepler AutoRegressive Planet Search (KARPS) project implements statistical methodology associated with autoregressive processes (in particular, ARIMA and ARFIMA) to model stellar lightcurves in order to improve exoplanet transit detection. We also develop a novel Transit Comb Filter (TCF) applied to the AR residuals which provides a periodogram analogous to the standard Box-fitting Least Squares (BLS) periodogram. We train a random forest classifier on known Kepler Objects of Interest (KOIs) using select features from different stages of this analysis, and then use ROC curves to define and calibrate the criteria to recover the KOI planet candidates with high fidelity. These statistical methods are detailed in a contributed poster (Feigelson et al., this meeting).These procedures are applied to the full DR25 dataset of NASA’s Kepler mission. Using the classification criteria, a vast majority of known KOIs are recovered and dozens of new KARPS Candidate Planets (KCPs) discovered, including ultra-short period exoplanets. The KCPs will be briefly presented and discussed.
DNBR Prediction Using a Support Vector Regression
International Nuclear Information System (INIS)
Yang, Heon Young; Na, Man Gyun
2008-01-01
PWRs (Pressurized Water Reactors) generally operate in the nucleate boiling state. However, the conversion of nucleate boiling into film boiling with conspicuously reduced heat transfer induces a boiling crisis that may cause the fuel clad melting in the long run. This type of boiling crisis is called Departure from Nucleate Boiling (DNB) phenomena. Because the prediction of minimum DNBR in a reactor core is very important to prevent the boiling crisis such as clad melting, a lot of research has been conducted to predict DNBR values. The object of this research is to predict minimum DNBR applying support vector regression (SVR) by using the measured signals of a reactor coolant system (RCS). The SVR has extensively and successfully been applied to nonlinear function approximation like the proposed problem for estimating DNBR values that will be a function of various input variables such as reactor power, reactor pressure, core mass flowrate, control rod positions and so on. The minimum DNBR in a reactor core is predicted using these various operating condition data as the inputs to the SVR. The minimum DBNR values predicted by the SVR confirm its correctness compared with COLSS values
Ata, Baris; Zeng, Xing; Son, Weon Y; Holzer, Hananel; Tan, Seang L
2011-11-01
The aim of this retrospective study was to compare the oocyte yield with the luteal estradiol patch (LPA) - GnRH antagonist and microdose (MD) flare-up protocols in anticipated poor responders. Fifty-seven women who underwent IVF treatment following stimulation with LPA or MD protocols at McGill Reproductive Centre were matched for age and markers of ovarian reserve. Numbers of oocytes collected (6 vs 7), mature oocytes collected (5 vs 5), and oocyte maturation rates (72% vs 74%) were similar. The numbers of good quality embryos available (2 vs 1) and embryos transferred (3 vs 3) were likewise similar. Embryo implantation rate of 16.7% and clinical pregnancy rate of 38.9% achieved in the LPA group were almost 50% higher than the corresponding figures at 10.3% and 22.2% in the MD group; however, the differences were not statistically significant (p > 0.05 for all comparisons). Although the results do not suggest an increased oocyte yield or follicular synchronization with the LPA protocol, the observed trend toward higher embryo implantation and clinical pregnancy rates requires further research.
Sirenomelia and severe caudal regression syndrome.
Seidahmed, Mohammed Z; Abdelbasit, Omer B; Alhussein, Khalid A; Miqdad, Abeer M; Khalil, Mohammed I; Salih, Mustafa A
2014-12-01
To describe cases of sirenomelia and severe caudal regression syndrome (CRS), to report the prevalence of sirenomelia, and compare our findings with the literature. Retrospective data was retrieved from the medical records of infants with the diagnosis of sirenomelia and CRS and their mothers from 1989 to 2010 (22 years) at the Security Forces Hospital, Riyadh, Saudi Arabia. A perinatologist, neonatologist, pediatric neurologist, and radiologist ascertained the diagnoses. The cases were identified as part of a study of neural tube defects during that period. A literature search was conducted using MEDLINE. During the 22-year study period, the total number of deliveries was 124,933 out of whom, 4 patients with sirenomelia, and 2 patients with severe forms of CRS were identified. All the patients with sirenomelia had single umbilical artery, and none were the infant of a diabetic mother. One patient was a twin, and another was one of triplets. The 2 patients with CRS were sisters, their mother suffered from type II diabetes mellitus and morbid obesity on insulin, and neither of them had a single umbilical artery. Other associated anomalies with sirenomelia included an absent radius, thumb, and index finger in one patient, Potter's syndrome, abnormal ribs, microphthalmia, congenital heart disease, hypoplastic lungs, and diaphragmatic hernia. The prevalence of sirenomelia (3.2 per 100,000) is high compared with the international prevalence of one per 100,000. Both cases of CRS were infants of type II diabetic mother with poor control, supporting the strong correlation of CRS and maternal diabetes.
Gaussian process regression for tool wear prediction
Kong, Dongdong; Chen, Yongjie; Li, Ning
2018-05-01
To realize and accelerate the pace of intelligent manufacturing, this paper presents a novel tool wear assessment technique based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and Gaussian process regression (GPR) for real-timely and accurately monitoring the in-process tool wear parameters (flank wear width). The KPCA_IRBF is a kind of new nonlinear dimension-increment technique and firstly proposed for feature fusion. The tool wear predictive value and the corresponding confidence interval are both provided by utilizing the GPR model. Besides, GPR performs better than artificial neural networks (ANN) and support vector machines (SVM) in prediction accuracy since the Gaussian noises can be modeled quantitatively in the GPR model. However, the existence of noises will affect the stability of the confidence interval seriously. In this work, the proposed KPCA_IRBF technique helps to remove the noises and weaken its negative effects so as to make the confidence interval compressed greatly and more smoothed, which is conducive for monitoring the tool wear accurately. Moreover, the selection of kernel parameter in KPCA_IRBF can be easily carried out in a much larger selectable region in comparison with the conventional KPCA_RBF technique, which helps to improve the efficiency of model construction. Ten sets of cutting tests are conducted to validate the effectiveness of the presented tool wear assessment technique. The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions. This study lays the foundation for tool wear monitoring in real industrial settings.
Keith, Timothy Z
2014-01-01
Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. Covers both MR and SEM, while explaining their relevance to one another Also includes path analysis, confirmatory factor analysis, and latent growth modeling Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: http://tzkeith.com/.
Alpha-induced instabilities in tandem thermal barriers
Energy Technology Data Exchange (ETDEWEB)
Kammash, T.; Galbraith, D.L.
1987-01-01
A major premise in the operation of Tandem Mirror reactors is that the fusion reactions take place in the central cell only. The alpha particles generated by the Deuterium-Tritium (DT) fusions, along with other ions, will however pass from the central cell to the thermal barriers and return to the central cell as a result of reflection by the potential hills that exist by the plugs' side of these barriers. This streaming motion gives rise to electrostatic and electomagnetic instabilities which could detract from the barrier's function as a thermal insulator. The number density and streaming velocity of these passing particles are dictated by the electrostatic potential variation and the magnetic field structure in these regions. It is shown that, in the absence of alphas, barriers with deep potential depression are less susceptible to electrostatic instabilities while particularly vulnerable to unstable electromagnetic modes. In the presence of alphas, especially the fast alphas whose mean energy is significantly larger than the barrier potentials they see, (which is twice as high as that seen by the ions) both types of modes become unstable.
Interferon-alpha induced depressive-like behavior in rats
DEFF Research Database (Denmark)
Fischer, C. W.; Liebenberg, N.; Elfving, B.
2013-01-01
to link inflammation and depression, such as increased levels of the neurotoxic tryptophan metabolite, quinolinic acid (QUIN), and decreased brain-derived neurotrophic factor (BDNF), a protein that plays an important role in survival, differentiation and growth of neurons. The successful development...
Interferon alpha induced cytogenetic remissions of chronic myelocytic leukemia
International Nuclear Information System (INIS)
Oguma, Nobuo; Shigeta, Chiharu; Tanaka, Kimio; Kamada, Nanao; Kuramoto, Atsushi; Ito, Chikako.
1994-01-01
In two heavily exposed A-bomb survivors, Philadelphia (Ph 1 ) chromosomes completed disappeared by the treatment with interferon (IFN)-α for chronic myelocytic leukemia (CML). One is a 55-year-old man exposed at 2.0 km in Hiroshima at the age of 13. Periodic mass screening in 1987 showed the presence of Ph 1 chromosomes, in addition to an increased number of leukocytes and the presence of neocytes. Subcutaneous injection of IFN-α (6,000,000 units/day) was started with a diagnosis of CML, and 5 months later Ph 1 chromosomes disappeared. Then, because Ph 1 chromosomes were found in 2.8% of 143 cells one year and 6 months after the termination of IFN-α, re-injection of IFN-α was started. The other patient is a 53-year-old woman exposed at 2.0 km in Hiroshima at the age of 6. She was pointed out to have leukocytosis in 1992; bone marrow examination showed the presence of Ph 1 chromosomes in 88.1% of 88 cells analyzed, leading to a diagnosis of CML. Subcutaneous injection of IFN-α (6,000,000 units/day) was started and 5 months later Ph 1 chromosomes disappeared. As of 11 months after the start of injection, no Ph 1 chromosomes were observed, but the patient is still treated with IFN-α injection. Effects of IFN-α and problems of residual Ph 1 chromosomes are discussed in a review of the literature. (N.K.)
Tumor necrosis factor-alpha induced enhancement of cryosurgery
Goel, Raghav; Paciotti, Guilio F.; Bischof, John C.
2008-02-01
Local recurrence of cancer after cryosurgery is related to the inability to monitor and predict destruction of cancer (temperatures > -40°C) within an iceball. We previously reported that a cytokine adjuvant TNF-α could be used to achieve complete cancer destruction at the periphery of an iceball (0 to -40°C). This study is a further development of that work in which cryosurgery was performed using cryoprobes operating at temperatures > -40°C. LNCaP Pro 5 tumor grown in a dorsal skin fold chamber (DSFC) was frozen at -6°C after TNF-α incubation for 4 or 24 hours. Tumors grown in the hind limb were frozen with a probe tip temperature of -40°C, 4 or 24 hours after systemic injection with TNF-α. Both cryosurgery alone or TNF-α treatment alone caused only a minimal damage to the tumor tissue at the conditions used in the study. The combination of TNF-α and cryosurgery produced a significant damage to the tumor tissue in both the DSFC and the hind limb model system. This augmentation in cryoinjury was found to be time-dependent with 4-hour time period between the two treatments being more effective than 24-hour. These results suggests the possibility of cryotreatment at temperatures > -40°C with the administration of TNF-α.
Kepler AutoRegressive Planet Search
Caceres, Gabriel Antonio; Feigelson, Eric
2016-01-01
The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; AR-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. The analysis procedures of the project are applied to a portion of the publicly available Kepler light curve data for the full 4-year mission duration. Tests of the methods have been made on a subset of Kepler Objects of Interest (KOI) systems, classified both as planetary `candidates' and `false positives' by the Kepler Team, as well as a random sample of unclassified systems. We find that the ARMA-type modeling successfully reduces the stellar variability, by a factor of 10 or more in active stars and by smaller factors in more quiescent stars. A typical quiescent Kepler star has an interquartile range (IQR) of ~10 e-/sec, which may improve slightly after modeling, while those with IQR ranging from 20 to 50 e-/sec, have improvements from 20% up to 70%. High activity stars (IQR exceeding 100) markedly improve. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. Our findings to date on real
Detection of epistatic effects with logic regression and a classical linear regression model.
Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata
2014-02-01
To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.
Sparse Regression by Projection and Sparse Discriminant Analysis
Qi, Xin; Luo, Ruiyan; Carroll, Raymond J.; Zhao, Hongyu
2015-01-01
predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths
Poisson Mixture Regression Models for Heart Disease Prediction.
Mufudza, Chipo; Erol, Hamza
2016-01-01
Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.
Dimension Reduction and Discretization in Stochastic Problems by Regression Method
DEFF Research Database (Denmark)
Ditlevsen, Ove Dalager
1996-01-01
The chapter mainly deals with dimension reduction and field discretizations based directly on the concept of linear regression. Several examples of interesting applications in stochastic mechanics are also given.Keywords: Random fields discretization, Linear regression, Stochastic interpolation, ...
Linear regression crash prediction models : issues and proposed solutions.
2010-05-01
The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...
An Additive-Multiplicative Cox-Aalen Regression Model
DEFF Research Database (Denmark)
Scheike, Thomas H.; Zhang, Mei-Jie
2002-01-01
Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects...
Logistic Regression Modeling of Diminishing Manufacturing Sources for Integrated Circuits
National Research Council Canada - National Science Library
Gravier, Michael
1999-01-01
.... The research identified logistic regression as a powerful tool for analysis of DMSMS and further developed twenty models attempting to identify the "best" way to model and predict DMSMS using logistic regression...
Model-based Quantile Regression for Discrete Data
Padellini, Tullia; Rue, Haavard
2018-01-01
Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite
The MIDAS Touch: Mixed Data Sampling Regression Models
Ghysels, Eric; Santa-Clara, Pedro; Valkanov, Rossen
2004-01-01
We introduce Mixed Data Sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Technically speaking MIDAS models specify conditional expectations as a distributed lag of regressors recorded at some higher sampling frequencies. We examine the asymptotic properties of MIDAS regression estimation and compare it with traditional distributed lag models. MIDAS regressions have wide applicability in macroeconomics and ï¿½nance.
Regression Benchmarking: An Approach to Quality Assurance in Performance
Bulej, Lubomír
2005-01-01
The paper presents a short summary of our work in the area of regression benchmarking and its application to software development. Specially, we explain the concept of regression benchmarking, the requirements for employing regression testing in a software project, and methods used for analyzing the vast amounts of data resulting from repeated benchmarking. We present the application of regression benchmarking on a real software project and conclude with a glimpse at the challenges for the fu...
Using Dominance Analysis to Determine Predictor Importance in Logistic Regression
Azen, Razia; Traxel, Nicole
2009-01-01
This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…
Meta-Modeling by Symbolic Regression and Pareto Simulated Annealing
Stinstra, E.; Rennen, G.; Teeuwen, G.J.A.
2006-01-01
The subject of this paper is a new approach to Symbolic Regression.Other publications on Symbolic Regression use Genetic Programming.This paper describes an alternative method based on Pareto Simulated Annealing.Our method is based on linear regression for the estimation of constants.Interval
Li, Jiangtong; Luo, Yongdao; Dai, Honglin
2018-01-01
Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR's analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR's result better than PLSR.
Morales, Esteban; de Leon, John Mark S; Abdollahi, Niloufar; Yu, Fei; Nouri-Mahdavi, Kouros; Caprioli, Joseph
2016-03-01
The study was conducted to evaluate threshold smoothing algorithms to enhance prediction of the rates of visual field (VF) worsening in glaucoma. We studied 798 patients with primary open-angle glaucoma and 6 or more years of follow-up who underwent 8 or more VF examinations. Thresholds at each VF location for the first 4 years or first half of the follow-up time (whichever was greater) were smoothed with clusters defined by the nearest neighbor (NN), Garway-Heath, Glaucoma Hemifield Test (GHT), and weighting by the correlation of rates at all other VF locations. Thresholds were regressed with a pointwise exponential regression (PER) model and a pointwise linear regression (PLR) model. Smaller root mean square error (RMSE) values of the differences between the observed and the predicted thresholds at last two follow-ups indicated better model predictions. The mean (SD) follow-up times for the smoothing and prediction phase were 5.3 (1.5) and 10.5 (3.9) years. The mean RMSE values for the PER and PLR models were unsmoothed data, 6.09 and 6.55; NN, 3.40 and 3.42; Garway-Heath, 3.47 and 3.48; GHT, 3.57 and 3.74; and correlation of rates, 3.59 and 3.64. Smoothed VF data predicted better than unsmoothed data. Nearest neighbor provided the best predictions; PER also predicted consistently more accurately than PLR. Smoothing algorithms should be used when forecasting VF results with PER or PLR. The application of smoothing algorithms on VF data can improve forecasting in VF points to assist in treatment decisions.
Regression: The Apple Does Not Fall Far From the Tree.
Vetter, Thomas R; Schober, Patrick
2018-05-15
Researchers and clinicians are frequently interested in either: (1) assessing whether there is a relationship or association between 2 or more variables and quantifying this association; or (2) determining whether 1 or more variables can predict another variable. The strength of such an association is mainly described by the correlation. However, regression analysis and regression models can be used not only to identify whether there is a significant relationship or association between variables but also to generate estimations of such a predictive relationship between variables. This basic statistical tutorial discusses the fundamental concepts and techniques related to the most common types of regression analysis and modeling, including simple linear regression, multiple regression, logistic regression, ordinal regression, and Poisson regression, as well as the common yet often underrecognized phenomenon of regression toward the mean. The various types of regression analysis are powerful statistical techniques, which when appropriately applied, can allow for the valid interpretation of complex, multifactorial data. Regression analysis and models can assess whether there is a relationship or association between 2 or more observed variables and estimate the strength of this association, as well as determine whether 1 or more variables can predict another variable. Regression is thus being applied more commonly in anesthesia, perioperative, critical care, and pain research. However, it is crucial to note that regression can identify plausible risk factors; it does not prove causation (a definitive cause and effect relationship). The results of a regression analysis instead identify independent (predictor) variable(s) associated with the dependent (outcome) variable. As with other statistical methods, applying regression requires that certain assumptions be met, which can be tested with specific diagnostics.
Few crystal balls are crystal clear : eyeballing regression
International Nuclear Information System (INIS)
Wittebrood, R.T.
1998-01-01
The theory of regression and statistical analysis as it applies to reservoir analysis was discussed. It was argued that regression lines are not always the final truth. It was suggested that regression lines and eyeballed lines are often equally accurate. The many conditions that must be fulfilled to calculate a proper regression were discussed. Mentioned among these conditions were the distribution of the data, hidden variables, knowledge of how the data was obtained, the need for causal correlation of the variables, and knowledge of the manner in which the regression results are going to be used. 1 tab., 13 figs
Sparse reduced-rank regression with covariance estimation
Chen, Lisha
2014-12-08
Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.
Sparse reduced-rank regression with covariance estimation
Chen, Lisha; Huang, Jianhua Z.
2014-01-01
Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.
Meaney, Christopher; Moineddin, Rahim
2014-01-24
In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the
Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro
2012-11-01
Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan. Copyright
Robust Regression and its Application in Financial Data Analysis
Mansoor Momeni; Mahmoud Dehghan Nayeri; Ali Faal Ghayoumi; Hoda Ghorbani
2010-01-01
This research is aimed to describe the application of robust regression and its advantages over the least square regression method in analyzing financial data. To do this, relationship between earning per share, book value of equity per share and share price as price model and earning per share, annual change of earning per share and return of stock as return model is discussed using both robust and least square regressions, and finally the outcomes are compared. Comparing the results from th...
Local bilinear multiple-output quantile/depth regression
Czech Academy of Sciences Publication Activity Database
Hallin, M.; Lu, Z.; Paindaveine, D.; Šiman, Miroslav
2015-01-01
Roč. 21, č. 3 (2015), s. 1435-1466 ISSN 1350-7265 R&D Projects: GA MŠk(CZ) 1M06047 Institutional support: RVO:67985556 Keywords : conditional depth * growth chart * halfspace depth * local bilinear regression * multivariate quantile * quantile regression * regression depth Subject RIV: BA - General Mathematics Impact factor: 1.372, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/siman-0446857.pdf
Mixture of Regression Models with Single-Index
Xiang, Sijia; Yao, Weixin
2016-01-01
In this article, we propose a class of semiparametric mixture regression models with single-index. We argue that many recently proposed semiparametric/nonparametric mixture regression models can be considered special cases of the proposed model. However, unlike existing semiparametric mixture regression models, the new pro- posed model can easily incorporate multivariate predictors into the nonparametric components. Backfitting estimates and the corresponding algorithms have been proposed for...
Alternative regression models to assess increase in childhood BMI
Beyerlein, Andreas; Fahrmeir, Ludwig; Mansmann, Ulrich; Toschke, André M
2008-01-01
Abstract Background Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 childre...
Preface to Berk's "Regression Analysis: A Constructive Critique"
de Leeuw, Jan
2003-01-01
It is pleasure to write a preface for the book ”Regression Analysis” of my fellow series editor Dick Berk. And it is a pleasure in particular because the book is about regression analysis, the most popular and the most fundamental technique in applied statistics. And because it is critical of the way regression analysis is used in the sciences, in particular in the social and behavioral sciences. Although the book can be read as an introduction to regression analysis, it can also be read as a...
A logistic regression estimating function for spatial Gibbs point processes
DEFF Research Database (Denmark)
Baddeley, Adrian; Coeurjolly, Jean-François; Rubak, Ege
We propose a computationally efficient logistic regression estimating function for spatial Gibbs point processes. The sample points for the logistic regression consist of the observed point pattern together with a random pattern of dummy points. The estimating function is closely related to the p......We propose a computationally efficient logistic regression estimating function for spatial Gibbs point processes. The sample points for the logistic regression consist of the observed point pattern together with a random pattern of dummy points. The estimating function is closely related...
Spontaneous regression of metastases from malignant melanoma: a case report
DEFF Research Database (Denmark)
Kalialis, Louise V; Drzewiecki, Krzysztof T; Mohammadi, Mahin
2008-01-01
A case of a 61-year-old male with widespread metastatic melanoma is presented 5 years after complete spontaneous cure. Spontaneous regression occurred in cutaneous, pulmonary, hepatic and cerebral metastases. A review of the literature reveals seven cases of regression of cerebral metastases......; this report is the first to document complete spontaneous regression of cerebral metastases from malignant melanoma by means of computed tomography scans. Spontaneous regression is defined as the partial or complete disappearance of a malignant tumour in the absence of all treatment or in the presence...
Acupuncture and Spontaneous Regression of a Radiculopathic Cervical Herniated Disc
Directory of Open Access Journals (Sweden)
Kim Sung-Ha
2012-06-01
Full Text Available The spontaneous regression of herniated cervical discs is not a well-established phenomenon. However, we encountered a case of a spontaneous regression of a severe radiculopathic herniated cervical disc that was treated with acupuncture, pharmacopuncture, and herb medicine. The symptoms were improved within 12 months of treatment. Magnetic resonance imaging (MRI conducted at that time revealed marked regression of the herniated disc. This case provides an additional example of spontaneous regression of a herniated cervical disc documented by MRI following non-surgical treatment.
Significance testing in ridge regression for genetic data
Directory of Open Access Journals (Sweden)
De Iorio Maria
2011-09-01
Full Text Available Abstract Background Technological developments have increased the feasibility of large scale genetic association studies. Densely typed genetic markers are obtained using SNP arrays, next-generation sequencing technologies and imputation. However, SNPs typed using these methods can be highly correlated due to linkage disequilibrium among them, and standard multiple regression techniques fail with these data sets due to their high dimensionality and correlation structure. There has been increasing interest in using penalised regression in the analysis of high dimensional data. Ridge regression is one such penalised regression technique which does not perform variable selection, instead estimating a regression coefficient for each predictor variable. It is therefore desirable to obtain an estimate of the significance of each ridge regression coefficient. Results We develop and evaluate a test of significance for ridge regression coefficients. Using simulation studies, we demonstrate that the performance of the test is comparable to that of a permutation test, with the advantage of a much-reduced computational cost. We introduce the p-value trace, a plot of the negative logarithm of the p-values of ridge regression coefficients with increasing shrinkage parameter, which enables the visualisation of the change in p-value of the regression coefficients with increasing penalisation. We apply the proposed method to a lung cancer case-control data set from EPIC, the European Prospective Investigation into Cancer and Nutrition. Conclusions The proposed test is a useful alternative to a permutation test for the estimation of the significance of ridge regression coefficients, at a much-reduced computational cost. The p-value trace is an informative graphical tool for evaluating the results of a test of significance of ridge regression coefficients as the shrinkage parameter increases, and the proposed test makes its production computationally feasible.
Regression calibration with more surrogates than mismeasured variables
Kipnis, Victor
2012-06-29
In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.
Regression calibration with more surrogates than mismeasured variables
Kipnis, Victor; Midthune, Douglas; Freedman, Laurence S.; Carroll, Raymond J.
2012-01-01
In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.
Cao, Qingqing; Wu, Zhenqiang; Sun, Ying; Wang, Tiezhu; Han, Tengwei; Gu, Chaomei; Sun, Yehuan
2011-11-01
To Eexplore the application of negative binomial regression and modified Poisson regression analysis in analyzing the influential factors for injury frequency and the risk factors leading to the increase of injury frequency. 2917 primary and secondary school students were selected from Hefei by cluster random sampling method and surveyed by questionnaire. The data on the count event-based injuries used to fitted modified Poisson regression and negative binomial regression model. The risk factors incurring the increase of unintentional injury frequency for juvenile students was explored, so as to probe the efficiency of these two models in studying the influential factors for injury frequency. The Poisson model existed over-dispersion (P Poisson regression and negative binomial regression model, was fitted better. respectively. Both showed that male gender, younger age, father working outside of the hometown, the level of the guardian being above junior high school and smoking might be the results of higher injury frequencies. On a tendency of clustered frequency data on injury event, both the modified Poisson regression analysis and negative binomial regression analysis can be used. However, based on our data, the modified Poisson regression fitted better and this model could give a more accurate interpretation of relevant factors affecting the frequency of injury.
Interpreting Bivariate Regression Coefficients: Going beyond the Average
Halcoussis, Dennis; Phillips, G. Michael
2010-01-01
Statistics, econometrics, investment analysis, and data analysis classes often review the calculation of several types of averages, including the arithmetic mean, geometric mean, harmonic mean, and various weighted averages. This note shows how each of these can be computed using a basic regression framework. By recognizing when a regression model…
Testing the equality of nonparametric regression curves based on ...
African Journals Online (AJOL)
Abstract. In this work we propose a new methodology for the comparison of two regression functions f1 and f2 in the case of homoscedastic error structure and a fixed design. Our approach is based on the empirical Fourier coefficients of the regression functions f1 and f2 respectively. As our main results we obtain the ...
General Nature of Multicollinearity in Multiple Regression Analysis.
Liu, Richard
1981-01-01
Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)
Prediction accuracy and stability of regression with optimal scaling transformations
Kooij, van der Anita J.
2007-01-01
The central topic of this thesis is the CATREG approach to nonlinear regression. This approach finds optimal quantifications for categorical variables and/or nonlinear transformations for numerical variables in regression analysis. (CATREG is implemented in SPSS Categories by the author of the
Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression
DEFF Research Database (Denmark)
Exterkate, Peter; Groenen, Patrick J.F.; Heij, Christiaan
This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the predi...
Tax Evasion, Information Reporting, and the Regressive Bias Hypothesis
DEFF Research Database (Denmark)
Boserup, Simon Halphen; Pinje, Jori Veng
A robust prediction from the tax evasion literature is that optimal auditing induces a regressive bias in effective tax rates compared to statutory rates. If correct, this will have important distributional consequences. Nevertheless, the regressive bias hypothesis has never been tested empirically...
Statistical analysis of sediment toxicity by additive monotone regression splines
Boer, de W.J.; Besten, den P.J.; Braak, ter C.J.F.
2002-01-01
Modeling nonlinearity and thresholds in dose-effect relations is a major challenge, particularly in noisy data sets. Here we show the utility of nonlinear regression with additive monotone regression splines. These splines lead almost automatically to the estimation of thresholds. We applied this
Teaching the Concept of Breakdown Point in Simple Linear Regression.
Chan, Wai-Sum
2001-01-01
Most introductory textbooks on simple linear regression analysis mention the fact that extreme data points have a great influence on ordinary least-squares regression estimation; however, not many textbooks provide a rigorous mathematical explanation of this phenomenon. Suggests a way to fill this gap by teaching students the concept of breakdown…
Moderation analysis using a two-level regression model.
Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott
2014-10-01
Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.
Spontaneous regression of metastases from malignant melanoma: a case report
DEFF Research Database (Denmark)
Kalialis, Louise V; Drzewiecki, Krzysztof T; Mohammadi, Mahin
2008-01-01
A case of a 61-year-old male with widespread metastatic melanoma is presented 5 years after complete spontaneous cure. Spontaneous regression occurred in cutaneous, pulmonary, hepatic and cerebral metastases. A review of the literature reveals seven cases of regression of cerebral metastases; thi...
Implicit collinearity effect in linear regression: Application to basal ...
African Journals Online (AJOL)
Collinearity of predictor variables is a severe problem in the least square regression analysis. It contributes to the instability of regression coefficients and leads to a wrong prediction accuracy. Despite these problems, studies are conducted with a large number of observed and derived variables linked with a response ...
Augmenting Data with Published Results in Bayesian Linear Regression
de Leeuw, Christiaan; Klugkist, Irene
2012-01-01
In most research, linear regression analyses are performed without taking into account published results (i.e., reported summary statistics) of similar previous studies. Although the prior density in Bayesian linear regression could accommodate such prior knowledge, formal models for doing so are absent from the literature. The goal of this…
A test for the parameters of multiple linear regression models ...
African Journals Online (AJOL)
A test for the parameters of multiple linear regression models is developed for conducting tests simultaneously on all the parameters of multiple linear regression models. The test is robust relative to the assumptions of homogeneity of variances and absence of serial correlation of the classical F-test. Under certain null and ...
Who Will Win?: Predicting the Presidential Election Using Linear Regression
Lamb, John H.
2007-01-01
This article outlines a linear regression activity that engages learners, uses technology, and fosters cooperation. Students generated least-squares linear regression equations using TI-83 Plus[TM] graphing calculators, Microsoft[C] Excel, and paper-and-pencil calculations using derived normal equations to predict the 2004 presidential election.…
Testing hypotheses for differences between linear regression lines
Stanley J. Zarnoch
2009-01-01
Five hypotheses are identified for testing differences between simple linear regression lines. The distinctions between these hypotheses are based on a priori assumptions and illustrated with full and reduced models. The contrast approach is presented as an easy and complete method for testing for overall differences between the regressions and for making pairwise...
A flexible fuzzy regression algorithm for forecasting oil consumption estimation
International Nuclear Information System (INIS)
Azadeh, A.; Khakestani, M.; Saberi, M.
2009-01-01
Oil consumption plays a vital role in socio-economic development of most countries. This study presents a flexible fuzzy regression algorithm for forecasting oil consumption based on standard economic indicators. The standard indicators are annual population, cost of crude oil import, gross domestic production (GDP) and annual oil production in the last period. The proposed algorithm uses analysis of variance (ANOVA) to select either fuzzy regression or conventional regression for future demand estimation. The significance of the proposed algorithm is three fold. First, it is flexible and identifies the best model based on the results of ANOVA and minimum absolute percentage error (MAPE), whereas previous studies consider the best fitted fuzzy regression model based on MAPE or other relative error results. Second, the proposed model may identify conventional regression as the best model for future oil consumption forecasting because of its dynamic structure, whereas previous studies assume that fuzzy regression always provide the best solutions and estimation. Third, it utilizes the most standard independent variables for the regression models. To show the applicability and superiority of the proposed flexible fuzzy regression algorithm the data for oil consumption in Canada, United States, Japan and Australia from 1990 to 2005 are used. The results show that the flexible algorithm provides accurate solution for oil consumption estimation problem. The algorithm may be used by policy makers to accurately foresee the behavior of oil consumption in various regions.
A Methodology for Generating Placement Rules that Utilizes Logistic Regression
Wurtz, Keith
2008-01-01
The purpose of this article is to provide the necessary tools for institutional researchers to conduct a logistic regression analysis and interpret the results. Aspects of the logistic regression procedure that are necessary to evaluate models are presented and discussed with an emphasis on cutoff values and choosing the appropriate number of…
Spontaneous and complete regression of a thoracic disc herniation
International Nuclear Information System (INIS)
Coevoet, V.; Benoudiba, F.; Doyon, D.; Lignieres, C.; Said, G.
1997-01-01
Spontaneous regression of disc herniation is well known but the mechanism is not clear. Some hypotheses have been made. We present here a large thoracic disc herniation diagnosed by MRI which completely regressed one year after a medical treatment with complete amendment of symptoms. (authors)
Multivariate Regression of Liver on Intestine of Mice: A ...
African Journals Online (AJOL)
FIRST LADY
pairs recovered. Linear, semi-logarithmic and logarithmic-logarithmic (log- log) regressions were performed. He chose the log-log curves because its variance was more uniform. The statistical comparison of .... E(U1| U2 = u2) is the regression function of U1 on U2, and Var (U1|U2 = u2) is the conditional covariance matrix.
Mixed Frequency Data Sampling Regression Models: The R Package midasr
Directory of Open Access Journals (Sweden)
Eric Ghysels
2016-08-01
Full Text Available When modeling economic relationships it is increasingly common to encounter data sampled at different frequencies. We introduce the R package midasr which enables estimating regression models with variables sampled at different frequencies within a MIDAS regression framework put forward in work by Ghysels, Santa-Clara, and Valkanov (2002. In this article we define a general autoregressive MIDAS regression model with multiple variables of different frequencies and show how it can be specified using the familiar R formula interface and estimated using various optimization methods chosen by the researcher. We discuss how to check the validity of the estimated model both in terms of numerical convergence and statistical adequacy of a chosen regression specification, how to perform model selection based on a information criterion, how to assess forecasting accuracy of the MIDAS regression model and how to obtain a forecast aggregation of different MIDAS regression models. We illustrate the capabilities of the package with a simulated MIDAS regression model and give two empirical examples of application of MIDAS regression.
application of multilinear regression analysis in modeling of soil
African Journals Online (AJOL)
Windows User
Accordingly [1, 3] in their work, they applied linear regression ... (MLRA) is a statistical technique that uses several explanatory ... order to check this, they adopted bivariate correlation analysis .... groups, namely A-1 through A-7, based on their relative expected ..... Multivariate Regression in Gorgan Province North of Iran” ...
Spatial correlation in Bayesian logistic regression with misclassification
DEFF Research Database (Denmark)
Bihrmann, Kristine; Toft, Nils; Nielsen, Søren Saxmose
2014-01-01
Standard logistic regression assumes that the outcome is measured perfectly. In practice, this is often not the case, which could lead to biased estimates if not accounted for. This study presents Bayesian logistic regression with adjustment for misclassification of the outcome applied to data...
Application of Negative Binomial Regression for Assessing Public ...
African Journals Online (AJOL)
Because the variance was nearly two times greater than the mean, the negative binomial regression model provided an improved fit to the data and accounted better for overdispersion than the Poisson regression model, which assumed that the mean and variance are the same. The level of education and race were found
Poisson Mixture Regression Models for Heart Disease Prediction
Erol, Hamza
2016-01-01
Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611
Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications
Directory of Open Access Journals (Sweden)
Guoqi Qian
2016-01-01
Full Text Available Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method.
Changes in persistence, spurious regressions and the Fisher hypothesis
DEFF Research Database (Denmark)
Kruse, Robinson; Ventosa-Santaulària, Daniel; Noriega, Antonio E.
Declining inflation persistence has been documented in numerous studies. When such series are analyzed in a regression framework in conjunction with other persistent time series, spurious regressions are likely to occur. We propose to use the coefficient of determination R2 as a test statistic to...
Predicting Word Reading Ability: A Quantile Regression Study
McIlraith, Autumn L.
2018-01-01
Predictors of early word reading are well established. However, it is unclear if these predictors hold for readers across a range of word reading abilities. This study used quantile regression to investigate predictive relationships at different points in the distribution of word reading. Quantile regression analyses used preschool and…
Directory of Open Access Journals (Sweden)
Flavell Richard A
2003-02-01
Full Text Available Abstract We recently demonstrated that caspase-3 is important for apoptosis during spontaneous involution of the corpus luteum (CL. These studies tested if prostaglandin F2α (PGF2α or FAS regulated luteal regression, utilize a caspase-3 dependent pathway to execute luteal cell apoptosis, and if the two receptors work via independent or potentially shared intracellular signaling components/pathways to activate caspase-3. Wild-type (WT or caspase-3 deficient female mice, 25–26 days old, were given 10 IU equine chorionic gonadotropin (eCG intraperitoneally (IP followed by 10 IU human chorionic gonadotropin (hCG IP 46 h later to synchronize ovulation. The animals were then injected with IgG (2 micrograms, i.v., the FAS-activating antibody Jo2 (2 micrograms, i.v., or PGF2α (10 micrograms, i.p. at 24 or 48 h post-ovulation. Ovaries from each group were collected 8 h later for assessment of active caspase-3 enzyme and apoptosis (measured by the TUNEL assay in the CL. Regardless of genotype or treatment, CL in ovaries collected from mice injected 24 h after ovulation showed no evidence of active caspase-3 or apoptosis. However, PGF2α or Jo2 at 48 h post-ovulation and collected 8 h later induced caspase-3 activation in 13.2 ± 1.8% and 13.7 ± 2.2 % of the cells, respectively and resulted in 16.35 ± 0.7% (PGF2α and 14.3 ± 2.5% TUNEL-positive cells when compared to 1.48 ± 0.8% of cells CL in IgG treated controls. In contrast, CL in ovaries collected from caspase-3 deficient mice whether treated with PGF2α , Jo2, or control IgG at 48 h post-ovulation showed little evidence of active caspase-3 or apoptosis. CL of WT mice treated with Jo2 at 48 h post-ovulation had an 8-fold increase in the activity of caspase-8, an activator of caspase-3 that is coupled to the FAS death receptor. Somewhat unexpectedly, however, treatment of WT mice with PGF2α at 48 h post-ovulation resulted in a 22-fold increase in caspase-8 activity in the CL, despite the fact
Regression away from the mean: Theory and examples.
Schwarz, Wolf; Reike, Dennis
2018-02-01
Using a standard repeated measures model with arbitrary true score distribution and normal error variables, we present some fundamental closed-form results which explicitly indicate the conditions under which regression effects towards (RTM) and away from the mean are expected. Specifically, we show that for skewed and bimodal distributions many or even most cases will show a regression effect that is in expectation away from the mean, or that is not just towards but actually beyond the mean. We illustrate our results in quantitative detail with typical examples from experimental and biometric applications, which exhibit a clear regression away from the mean ('egression from the mean') signature. We aim not to repeal cautionary advice against potential RTM effects, but to present a balanced view of regression effects, based on a clear identification of the conditions governing the form that regression effects take in repeated measures designs. © 2017 The British Psychological Society.
Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model
Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami
2017-06-01
A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.
Tutorial on Using Regression Models with Count Outcomes Using R
Directory of Open Access Journals (Sweden)
A. Alexander Beaujean
2016-02-01
Full Text Available Education researchers often study count variables, such as times a student reached a goal, discipline referrals, and absences. Most researchers that study these variables use typical regression methods (i.e., ordinary least-squares either with or without transforming the count variables. In either case, using typical regression for count data can produce parameter estimates that are biased, thus diminishing any inferences made from such data. As count-variable regression models are seldom taught in training programs, we present a tutorial to help educational researchers use such methods in their own research. We demonstrate analyzing and interpreting count data using Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. The count regression methods are introduced through an example using the number of times students skipped class. The data for this example are freely available and the R syntax used run the example analyses are included in the Appendix.
Crawford, John R.; Garthwaite, Paul H.; Denham, Annie K.; Chelune, Gordon J.
2012-01-01
Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because…
Koon, Sharon; Petscher, Yaacov
2015-01-01
The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by…
Impact of multicollinearity on small sample hydrologic regression models
Kroll, Charles N.; Song, Peter
2013-06-01
Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.
Background stratified Poisson regression analysis of cohort data.
Richardson, David B; Langholz, Bryan
2012-03-01
Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.
Use of probabilistic weights to enhance linear regression myoelectric control.
Smith, Lauren H; Kuiken, Todd A; Hargrove, Levi J
2015-12-01
Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts' law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p linear regression control. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.
Independent contrasts and PGLS regression estimators are equivalent.
Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary
2012-05-01
We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.
Background stratified Poisson regression analysis of cohort data
International Nuclear Information System (INIS)
Richardson, David B.; Langholz, Bryan
2012-01-01
Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models. (orig.)
Developmental regression in autism: research and conceptual questions
Directory of Open Access Journals (Sweden)
Carolina Lampreia
2013-11-01
Full Text Available The subject of developmental regression in autism has gained importance and a growing number of studies have been conducted in recent years. It is a major issue indicating that there is not a unique form of autism onset. However the phenomenon itself and the concept of regression have been the subject of some debate: there is no consensus on the existence of regression, as there is no consensus on its definition. The aim of this paper is to review the research literature in this area and to introduce some conceptual questions about its existence and its definition.
On weighted and locally polynomial directional quantile regression
Czech Academy of Sciences Publication Activity Database
Boček, Pavel; Šiman, Miroslav
2017-01-01
Roč. 32, č. 3 (2017), s. 929-946 ISSN 0943-4062 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : Quantile regression * Nonparametric regression * Nonparametric regression Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 0.434, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/bocek-0458380.pdf
Short-term load forecasting with increment regression tree
Energy Technology Data Exchange (ETDEWEB)
Yang, Jingfei; Stenzel, Juergen [Darmstadt University of Techonology, Darmstadt 64283 (Germany)
2006-06-15
This paper presents a new regression tree method for short-term load forecasting. Both increment and non-increment tree are built according to the historical data to provide the data space partition and input variable selection. Support vector machine is employed to the samples of regression tree nodes for further fine regression. Results of different tree nodes are integrated through weighted average method to obtain the comprehensive forecasting result. The effectiveness of the proposed method is demonstrated through its application to an actual system. (author)
Least Squares Adjustment: Linear and Nonlinear Weighted Regression Analysis
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg
2007-01-01
This note primarily describes the mathematics of least squares regression analysis as it is often used in geodesy including land surveying and satellite positioning applications. In these fields regression is often termed adjustment. The note also contains a couple of typical land surveying...... and satellite positioning application examples. In these application areas we are typically interested in the parameters in the model typically 2- or 3-D positions and not in predictive modelling which is often the main concern in other regression analysis applications. Adjustment is often used to obtain...... the clock error) and to obtain estimates of the uncertainty with which the position is determined. Regression analysis is used in many other fields of application both in the natural, the technical and the social sciences. Examples may be curve fitting, calibration, establishing relationships between...
Regularized multivariate regression models with skew-t error distributions
Chen, Lianfu; Pourahmadi, Mohsen; Maadooliat, Mehdi
2014-01-01
We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both
A gentle introduction to quantile regression for ecologists
Cade, B.S.; Noon, B.R.
2003-01-01
Quantile regression is a way to estimate the conditional quantiles of a response variable distribution in the linear model that provides a more complete view of possible causal relationships between variables in ecological processes. Typically, all the factors that affect ecological processes are not measured and included in the statistical models used to investigate relationships between variables associated with those processes. As a consequence, there may be a weak or no predictive relationship between the mean of the response variable (y) distribution and the measured predictive factors (X). Yet there may be stronger, useful predictive relationships with other parts of the response variable distribution. This primer relates quantile regression estimates to prediction intervals in parametric error distribution regression models (eg least squares), and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of the estimates for homogeneous and heterogeneous regression models.
Examination of influential observations in penalized spline regression
Türkan, Semra
2013-10-01
In parametric or nonparametric regression models, the results of regression analysis are affected by some anomalous observations in the data set. Thus, detection of these observations is one of the major steps in regression analysis. These observations are precisely detected by well-known influence measures. Pena's statistic is one of them. In this study, Pena's approach is formulated for penalized spline regression in terms of ordinary residuals and leverages. The real data and artificial data are used to see illustrate the effectiveness of Pena's statistic as to Cook's distance on detecting influential observations. The results of the study clearly reveal that the proposed measure is superior to Cook's Distance to detect these observations in large data set.
MODELING SNAKE MICROHABITAT FROM RADIOTELEMETRY STUDIES USING POLYTOMOUS LOGISTIC REGRESSION
Multivariate analysis of snake microhabitat has historically used techniques that were derived under assumptions of normality and common covariance structure (e.g., discriminant function analysis, MANOVA). In this study, polytomous logistic regression (PLR which does not require ...
Testing for Stock Market Contagion: A Quantile Regression Approach
S.Y. Park (Sung); W. Wang (Wendun); N. Huang (Naijing)
2015-01-01
markdownabstract__Abstract__ Regarding the asymmetric and leptokurtic behavior of financial data, we propose a new contagion test in the quantile regression framework that is robust to model misspecification. Unlike conventional correlation-based tests, the proposed quantile contagion test
Correlation-regression model for physico-chemical quality of ...
African Journals Online (AJOL)
abusaad
areas, suggesting that groundwater quality in urban areas is closely related with land use ... the ground water, with correlation and regression model is also presented. ...... WHO (World Health Organization) (1985). Health hazards from nitrates.
Sunspot Cycle Prediction Using Multivariate Regression and Binary ...
Indian Academy of Sciences (India)
49
Multivariate regression model has been derived based on the available cycles 1 .... The flare index correlates well with various parameters of the solar activity. ...... 32) Sabarinath A and Anilkumar A K 2011 A stochastic prediction model for the.